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Edit and run 





In [1]:



    


import pandas as pd


class GeoDataCensal(object):
    """GeoData of the censo information. There is information of the codes,
    names, censo and geometry.

    """

    def __init__(self, data_censal, var_hierarchy):
        self._check_inputs(data_censal, var_hierarchy)
        self.data_censal = data_censal
        self.var_hierarchy = var_hierarchy
        self.levels = len(var_hierarchy['codes'])

    def _check_inputs(self, data_censal, var_hierarchy):
        assert('geometry' in var_hierarchy)
        assert('codes' in var_hierarchy)
        assert('names' in var_hierarchy)
        assert('censo' in var_hierarchy)
        assert(isinstance(var_hierarchy['geometry'], str))
        assert(isinstance(var_hierarchy['codes'], list))
        assert(isinstance(var_hierarchy['names'], list))
        assert(isinstance(var_hierarchy['censo'], list))
        assert(len(var_hierarchy['codes']) == len(var_hierarchy['names']))

    def _get_columns_level(self, level, outer=False):
        outer_cols = []
        if outer:
            outer_cols = self.outer_cols
        geo_cols = [self.var_hierarchy['geometry']]
        if level == 0:
            columns = outer_cols+geo_cols
        elif level > self.levels or level < 0:
            msg = "Not correct 'level' input."
            raise IndexError(msg)
        else:
            names_cols = self.var_hierarchy['names'][self.levels-level:]
            codes_cols = self.var_hierarchy['codes'][self.levels-level:]
            columns = list(set(outer_cols+names_cols+codes_cols+geo_cols))
        return columns

    def _filter4level(self, level, outer=False):
        return self.data_censal[self._get_columns_level(level, outer)]

    @classmethod
    def _data_reduction(cls, data_censo, var_hierarchy):
        return cls(data_censo, var_hierarchy)

    @property
    def inner_cols(self):
        inner_cols = [var_hierarchy['geometry']]
        inner_cols += var_hierarchy['codes']
        inner_cols += var_hierarchy['names']
        inner_cols += var_hierarchy['censo']
        inner_cols = list(set(inner_cols))
        return inner_cols

    @property
    def outer_cols(self):
        outer_cols = [col for col in self.data_censal
                      if col not in self.inner_cols]
        return outer_cols

    def _filter_censo_by_level(self, level, outer=False):
        data_censo = self._filter4level(level, outer)
        pivot_lvl_col = self.var_hierarchy['codes'][self.levels-level]
        data = data_censo.dissolve(by=pivot_lvl_col).reset_index(level=0)
        if self.var_hierarchy['censo']:
            data_censal = self.data_censal[[pivot_lvl_col]]
            vars_censo = self.var_hierarchy['censo']
            data_censal[vars_censo] = self.data_censal[vars_censo]
            data_cns = data_censal.dissolve(by=pivot_lvl_col,
                                            aggfunc="sum")
            data_cns = data_cns.reset_index(level=0)
            data = pd.merge(data, data_cns, on=pivot_lvl_col)
        return data

    def _filter_hierharchy_by_level(self, level):
        var_hierarchy = {}
        var_hierarchy['geometry'] = self.var_hierarchy['geometry']
        var_hierarchy['censo'] = self.var_hierarchy['censo']
        pivot_lvl = self.levels-level
        var_hierarchy['codes'] = self.var_hierarchy['codes'][pivot_lvl:]
        var_hierarchy['names'] = self.var_hierarchy['names'][pivot_lvl:]
        return var_hierarchy

    def filter_data_by_level(self, level, outer=False, raw=False):
        data_censo = self._filter_censo_by_level(level, outer)
        var_hierarchy = self._filter_hierharchy_by_level(level)
        if raw:
            return data_censo, var_hierarchy
        else:
            return self._data_reduction(data_censo, var_hierarchy)


class Votations(object):
    """Votations data. The data of the results of votes by party in columns.
    Each columns has a party and some codes. The result is the votes.

    """

    def __init__(self, votes, var_info, collapse_ways):
        self._check_inputs(votes, var_info, collapse_ways)
        self.collapses = collapse_ways
        self.var_info = var_info
        self.votes = votes

    def _check_inputs(self, votes, var_info, collapse_ways):
        assert('codes' in var_info)
        assert('parties' in var_info)
        assert(isinstance(var_info['parties'], list))
        assert(isinstance(var_info['codes'], list))
        assert(isinstance(collapse_ways, dict))
        for w, ps in collapse_ways.items():
            assert(all([p in var_info['parties'] for p in ps]))

    def get_results(self, code, collapse_way=None):
        if collapse_way is None:
            collapsing = dict(zip(self.var_info['parties'],
                                  self.var_info['parties']))
        else:
            collapsing = self.collapses[collapse_way]
        votes_collapse = self.votes[[code]]
        for group, parties in collapsing.items():
            votes_collapse[group] = self.votes[parties].sum(1)
        votes = votes_collapse.dissolve(by=code, aggfunc='sum')
        votes = votes.reset_index(level=0)
        return votes



    











In [2]:



    


import os
import numpy as np
import pandas as pd
import geopandas as gpd
import folium

import matplotlib
%matplotlib inline



    











In [3]:



    


from splitters import Splitters
from parsing_utils import parse_eleccions_2015_csv, collapse_votes, collapse_info_mesas, left_filter_function



    











In [4]:



    


file_geojson = '/home/tono/code/Eleccions2017Cat/data/trio_llobregat_2015.geojson'
data_censo = gpd.read_file(file_geojson)



    











In [5]:



    


votos_2015_file = '/home/tono/code/Eleccions2017Cat/data/A20151_ME/Columnes_A20151_ME_ca_ES.csv'
data_info2015, votos2015 = parse_eleccions_2015_csv(votos_2015_file)



    











In [6]:



    


votos2015 = collapse_votes(votos2015)
data_info2015 = collapse_info_mesas(data_info2015)



    











In [7]:



    


data_info2015.columns



    


















    
Out[7]:








Index([u'CUSEC', u'Codi Prov�ncia', u'Nom Prov�ncia', u'Codi Vegueria',
       u'Nom Vegueria', u'Codi Comarca', u'Nom Comarca', u'Codi Municipi',
       u'Nom Municipi', u'Districte', u'Secci�', u'Cens electoral',
       u'Participaci� 13:00', u'Participaci� 18.00', u'Participaci� 20.00',
       u'Abstenci�', u'Vots nuls', u'Vots en blanc', u'Vots a candidatures',
       u'Vots v�lids'],
      dtype='object')

















In [8]:



    


trio_cities = ["Cornell\xe0 de Llobregat", "Esplugues de Llobregat",
               "Hospitalet de Llobregat, l'"]
codes_trio = {'Cornella': '08073',
              'Esplugues': '08077',
              'Hospitalet': '08101'}
def cmun_filter(data, cumuns):
    return data.loc[data.CUSEC.apply(lambda x: x[:5] in cumuns)]
trio_data_info2015 = cmun_filter(data_info2015, codes_trio.values())
trio_votos2015 = cmun_filter(votos2015, codes_trio.values())

Check not concident keys





In [9]:



    


votos2015_trio_ll, codes_not_in_censo, codes_not_votos2015 =\
    left_filter_function(data_censo, trio_votos2015, filter_code='CUSEC')

codes_not_in_censo, codes_not_votos2015



    


















    






parsing_utils.py:82: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  to_filter_data[filter_code] = to_filter_data[filter_code].apply(to_str)










    
Out[9]:








(set(), set())

















In [10]:



    


trio_data_info2015.loc[trio_data_info2015.CUSEC.apply(lambda x: x in ['0810103026', '0810103027']), :]



    


















    
Out[10]:










  
    

      
      CUSEC
      Codi Prov�ncia
      Nom Prov�ncia
      Codi Vegueria
      Nom Vegueria
      Codi Comarca
      Nom Comarca
      Codi Municipi
      Nom Municipi
      Districte
      Secci�
      Cens electoral
      Participaci� 13:00
      Participaci� 18.00
      Participaci� 20.00
      Abstenci�
      Vots nuls
      Vots en blanc
      Vots a candidatures
      Vots v�lids
    

  
  
    

      1752
      0810103026
      8
      Barcelona
      1
      Barcelona
      13
      Barcelon�s
      8101
      Hospitalet de Llobregat, l'
      3
      26
      663
      220
      426
      543
      120
      1
      2
      540
      542
    

    

      1753
      0810103027
      8
      Barcelona
      1
      Barcelona
      13
      Barcelon�s
      8101
      Hospitalet de Llobregat, l'
      3
      27
      902
      266
      574
      732
      170
      2
      1
      729
      730

Manual checking data





In [75]:



    


lh_territories = data_info2015.iloc[:, 7].apply(lambda x: x in [trio_cities[2]])
codes_data_info = data_info2015.iloc[np.where(lh_territories)[0], [0, 2, 4, 6, 8, 9, 10, 20]]
data_filtered = codes_data_info.groupby(by='CUSEC').first()
postfilter = data_filtered['Districte'].apply(lambda x: x == 2)
len(data_filtered.iloc[np.where(postfilter)[0], :-1])



    


















    
Out[75]:








37

















In [123]:



    


lh_territories = data_censo['CUMUN'].apply(lambda x: x in [codes_trio['Hospitalet']])
geos_data = data_censo.loc[lh_territories, :]
postfilter = geos_data['CDIS'].apply(lambda x: x == '03')
geos_data.iloc[np.where(postfilter)[0], :-1]



    


















    
Out[123]:










  
    

      
      OBJECTID
      CUSEC
      CUMUN
      CSEC
      CDIS
      CMUN
      CPRO
      CCA
      CUDIS
      OBS
      ...
      CNUT1
      CNUT2
      CNUT3
      CLAU2
      NPRO
      NCA
      NMUN
      Shape_Leng
      Shape_area
      Shape_len
    

  
  
    

      188
      5468
      0810103001
      08101
      001
      03
      101
      08
      09
      0810103
      
      ...
      5
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      2123.890820
      233567.718319
      2123.890820
    

    

      189
      5469
      0810103002
      08101
      002
      03
      101
      08
      09
      0810103
      
      ...
      5
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      833.710360
      35996.566769
      833.710360
    

    

      190
      5470
      0810103003
      08101
      003
      03
      101
      08
      09
      0810103
      
      ...
      5
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      610.232224
      21545.110651
      610.232224
    

    

      191
      5471
      0810103004
      08101
      004
      03
      101
      08
      09
      0810103
      
      ...
      5
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      1802.861597
      151432.401799
      1802.861597
    

    

      192
      5472
      0810103005
      08101
      005
      03
      101
      08
      09
      0810103
      
      ...
      5
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      1024.749970
      40664.451741
      1024.749970
    

    

      193
      5473
      0810103006
      08101
      006
      03
      101
      08
      09
      0810103
      
      ...
      5
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      1035.388672
      43244.099506
      1035.388672
    

    

      194
      5474
      0810103007
      08101
      007
      03
      101
      08
      09
      0810103
      
      ...
      5
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      543.880699
      18468.786956
      543.880699
    

    

      195
      5475
      0810103008
      08101
      008
      03
      101
      08
      09
      0810103
      
      ...
      5
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      1115.305583
      65292.507486
      1115.305583
    

    

      196
      5476
      0810103009
      08101
      009
      03
      101
      08
      09
      0810103
      
      ...
      5
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      919.712402
      39517.730602
      919.712402
    

    

      197
      5477
      0810103010
      08101
      010
      03
      101
      08
      09
      0810103
      
      ...
      5
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      504.786206
      15370.295343
      504.786206
    

    

      198
      5478
      0810103011
      08101
      011
      03
      101
      08
      09
      0810103
      
      ...
      5
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      1033.521147
      24979.467858
      1033.521147
    

    

      199
      5479
      0810103012
      08101
      012
      03
      101
      08
      09
      0810103
      
      ...
      5
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      912.163598
      48383.942635
      912.163598
    

    

      200
      5480
      0810103013
      08101
      013
      03
      101
      08
      09
      0810103
      
      ...
      5
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      1326.576905
      57562.768063
      1326.576905
    

    

      201
      5481
      0810103014
      08101
      014
      03
      101
      08
      09
      0810103
      
      ...
      5
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      1061.157893
      45949.590972
      1061.157893
    

    

      202
      5482
      0810103015
      08101
      015
      03
      101
      08
      09
      0810103
      
      ...
      5
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      1046.208852
      45640.896630
      1046.208852
    

    

      203
      5483
      0810103016
      08101
      016
      03
      101
      08
      09
      0810103
      
      ...
      5
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      1356.737957
      113246.660063
      1356.737957
    

    

      204
      5484
      0810103017
      08101
      017
      03
      101
      08
      09
      0810103
      
      ...
      5
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      529.655587
      15646.693621
      529.655587
    

    

      205
      5485
      0810103018
      08101
      018
      03
      101
      08
      09
      0810103
      
      ...
      5
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      961.853242
      43590.265904
      961.853242
    

    

      206
      5486
      0810103019
      08101
      019
      03
      101
      08
      09
      0810103
      
      ...
      5
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      1017.705873
      58164.603286
      1017.705873
    

    

      207
      5487
      0810103020
      08101
      020
      03
      101
      08
      09
      0810103
      
      ...
      5
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      886.459972
      42433.660350
      886.459972
    

    

      208
      5488
      0810103021
      08101
      021
      03
      101
      08
      09
      0810103
      
      ...
      5
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      747.448059
      28130.035120
      747.448059
    

    

      209
      5489
      0810103022
      08101
      022
      03
      101
      08
      09
      0810103
      
      ...
      5
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      683.869802
      18959.425685
      683.869802
    

    

      210
      5490
      0810103023
      08101
      023
      03
      101
      08
      09
      0810103
      
      ...
      5
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      1228.279958
      77602.416086
      1228.279958
    

    

      211
      5491
      0810103024
      08101
      024
      03
      101
      08
      09
      0810103
      
      ...
      5
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      677.843717
      16294.188666
      677.843717
    

    

      212
      5492
      0810103025
      08101
      025
      03
      101
      08
      09
      0810103
      
      ...
      5
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      745.823732
      33192.497465
      745.823732
    

  



25 rows × 21 columns

Mapping





In [11]:



    


d = trio_votos2015.iloc[:, [5, 8]].sum(1)/trio_votos2015.iloc[:, 1:].sum(1)
proind = pd.DataFrame(d, columns=['ProInd'])
proind = pd.concat([trio_votos2015['CUSEC'], proind], axis=1)



    











In [12]:



    


def choropleth_map(center_loc, zoom_start, file_geojson, results, key_on, key_data):
    m = folium.Map(center_loc, tiles='cartodbpositron',
                   zoom_start=zoom_start)
    columns = [key_on, key_data]
    key_on_v = 'feature.properties.' + key_on
    m.choropleth(
        geo_data=file_geojson,
        data=results,
        columns=columns,
        key_on=key_on_v,
        fill_color='YlGn',
        highlight=True,
        )
    return m

choropleth_map([41.37, 2.1], 13, file_geojson, proind, 'CUSEC', 'ProInd')



    


















    
Out[12]:
























In [ ]:



    






    











In [13]:



    


import seaborn as sns
import matplotlib


def results_map(center_loc, zoom_start, geodata, results, key_on, key_data):
    m = folium.Map(center_loc, tiles='cartodbpositron',
                   zoom_start=zoom_start)

    n_splits = 10
    par_splits = {'n_splits': n_splits, 'limits': (0, 1),
                  'open_limits': False, 'reverse': False}
    splitter = Splitters('equispaced_splitters', par_splits,
                         ranges=list(range(n_splits)))
    colors = sns.color_palette('RdYlGn', n_splits)

    def color_map(x):
        return matplotlib.colors.rgb2hex(colors[splitter(x)])

    def create_function_style(features):
        style_function = lambda x: {'fillColor': color_map(features),
                                    'fillOpacity': 0.4,
                                    'color': 'black',
                                    'weight': 0.5,
                                    'opacity': 1}
        return style_function


    highlight_function = lambda x: {'weight': 1.5,
                                    'fillOpacity': 0.75}

    for i in range(len(data_censo)):
        geodata_i = geodata.iloc[[i], :]
        key_on_i = geodata_i[key_on].ravel()[0]
        idxs = np.where(results[key_on].apply(lambda x: x == key_on_i))[0]
        if not len(idxs):
            continue
        idx_key = idxs[0]
        style_function = create_function_style(results.iloc[idx_key, :][key_data])
        geo_cens = folium.folium.GeoJson(geodata_i,
                                         style_function=style_function,
                                         highlight_function=highlight_function,
                                         overlay=True,
                                         control=False)
        geo_cens.add_child(folium.features.Popup(key_on_i))
        geo_cens.add_to(m, geodata.iloc[i, :][key_on])
    return m

results_map([41.37, 2.1], 13, data_censo, proind, 'CUSEC', 'ProInd')



    


















    
Out[13]:
























In [ ]:



    






    











In [157]:



    


np.where(proind.CUSEC.apply(lambda x: x == data_censo.CUSEC.iloc[324]))[0]



    


















    
Out[157]:








array([285])

















In [162]:



    


geodata_i = data_censo.iloc[[0], :]
key_on_i = geodata_i['CUSEC']



    











In [7]:



    


data_censo



    


















    
Out[7]:










  
    

      
      OBJECTID
      CUSEC
      CUMUN
      CSEC
      CDIS
      CMUN
      CPRO
      CCA
      CUDIS
      OBS
      ...
      CNUT2
      CNUT3
      CLAU2
      NPRO
      NCA
      NMUN
      Shape_Leng
      Shape_area
      Shape_len
      geometry
    

  
  
    

      0
      5155
      0807301001
      08073
      001
      01
      073
      08
      09
      0807301
      
      ...
      1
      1
      08073
      Barcelona
      Catalua
      Cornell de Llobregat
      1352.569111
      9.218862e+04
      1352.569111
      POLYGON ((2.07086332857145 41.35740405853809, ...
    

    

      1
      5156
      0807301002
      08073
      002
      01
      073
      08
      09
      0807301
      
      ...
      1
      1
      08073
      Barcelona
      Catalua
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      ...
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      Barcelona
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      09
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      ...
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      POLYGON ((2.063546649405958 41.35903638480863,...
    

    

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      09
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      ...
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      08073
      Barcelona
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      Cornell de Llobregat
      978.800293
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      POLYGON ((2.059556796436511 41.35907671405715,...
    

    

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      5180
      0807304001
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      09
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      ...
      1
      1
      08073
      Barcelona
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      901.157225
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      5181
      0807304002
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      09
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      ...
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      1
      08073
      Barcelona
      Catalua
      Cornell de Llobregat
      715.013054
      1.744408e+04
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      POLYGON ((2.07694456943873 41.35708127848684, ...
    

    

      27
      5182
      0807304003
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      09
      0807304
      
      ...
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      1
      08073
      Barcelona
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      Cornell de Llobregat
      772.422503
      3.520813e+04
      772.422503
      POLYGON ((2.073510737054216 41.36047889484263,...
    

    

      28
      5183
      0807304004
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      09
      0807304
      
      ...
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      1
      08073
      Barcelona
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      1452.359958
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      POLYGON ((2.080737635651198 41.35739498245629,...
    

    

      29
      5184
      0807304005
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      09
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      ...
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      1
      08073
      Barcelona
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      Cornell de Llobregat
      563.138566
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      POLYGON ((2.080048319263547 41.35832426126629,...
    

    

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

    

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      5575
      0810106003
      08101
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      101
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      09
      0810106
      
      ...
      1
      1
      08101
      Barcelona
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      465.907130
      1.222302e+04
      465.907130
      POLYGON ((2.107346478362054 41.35299296880945,...
    

    

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      5576
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      09
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      ...
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      1
      08101
      Barcelona
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      Hospitalet de Llobregat, L'
      396.592428
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      POLYGON ((2.10749779200804 41.34928961216207, ...
    

    

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      5577
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      101
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      09
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      ...
      1
      1
      08101
      Barcelona
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      Hospitalet de Llobregat, L'
      463.988312
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      463.988312
      POLYGON ((2.109295564176914 41.35195179616392,...
    

    

      298
      5578
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      09
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      ...
      1
      1
      08101
      Barcelona
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      Hospitalet de Llobregat, L'
      489.033456
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      POLYGON ((2.108177365059811 41.34770648802841,...
    

    

      299
      5579
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      09
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      ...
      1
      1
      08101
      Barcelona
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      Hospitalet de Llobregat, L'
      625.599616
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      POLYGON ((2.109390191493859 41.34787372860216,...
    

    

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      5580
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      ...
      1
      1
      08101
      Barcelona
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      Hospitalet de Llobregat, L'
      773.734189
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      773.734189
      POLYGON ((2.116906950356888 41.3518333115553, ...
    

    

      301
      5581
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      09
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      ...
      1
      1
      08101
      Barcelona
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      Hospitalet de Llobregat, L'
      578.463503
      1.556329e+04
      578.463503
      POLYGON ((2.1121606071758 41.35678368835044, 2...
    

    

      302
      5582
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      101
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      09
      0810106
      
      ...
      1
      1
      08101
      Barcelona
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      Hospitalet de Llobregat, L'
      775.161620
      2.240764e+04
      775.161620
      POLYGON ((2.115694358677211 41.3550198169447, ...
    

    

      303
      5583
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      101
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      09
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      ...
      1
      1
      08101
      Barcelona
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      Hospitalet de Llobregat, L'
      1316.293472
      7.186857e+04
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      POLYGON ((2.115942307489964 41.35669826603879,...
    

    

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      5584
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      101
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      09
      0810106
      
      ...
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      591.985358
      2.196809e+04
      591.985358
      POLYGON ((2.115731249011137 41.35532242585489,...
    

    

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      5585
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      09
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      ...
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      608.530782
      1.743800e+04
      608.530782
      POLYGON ((2.115576768962717 41.35405613070908,...
    

    

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      5586
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      101
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      09
      0810106
      
      ...
      1
      1
      08101
      Barcelona
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      Hospitalet de Llobregat, L'
      1037.075497
      4.908920e+04
      1037.075497
      POLYGON ((2.115341952095644 41.3521170374316, ...
    

    

      307
      5587
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      101
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      09
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      ...
      1
      1
      08101
      Barcelona
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      Hospitalet de Llobregat, L'
      899.058499
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      POLYGON ((2.114575876857076 41.35080843961178,...
    

    

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      5588
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      016
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      101
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      09
      0810106
      
      ...
      1
      1
      08101
      Barcelona
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      Hospitalet de Llobregat, L'
      8663.704846
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      POLYGON ((2.133252597556686 41.35985738233334,...
    

    

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      09
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      ...
      1
      1
      08101
      Barcelona
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      Hospitalet de Llobregat, L'
      474.399297
      1.150945e+04
      474.399297
      POLYGON ((2.115526284594824 41.35364240699773,...
    

    

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      5590
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      018
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      101
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      09
      0810106
      
      ...
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      385.126810
      8.786430e+03
      385.126810
      POLYGON ((2.109157519956944 41.35219010358173,...
    

    

      311
      5591
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      101
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      09
      0810106
      
      ...
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      901.677412
      4.862763e+04
      901.677412
      POLYGON ((2.111164050408914 41.35010261037827,...
    

    

      312
      5592
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      020
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      101
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      09
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      ...
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      630.123767
      1.990349e+04
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      POLYGON ((2.109694363132621 41.35353451084169,...
    

    

      313
      5593
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      021
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      101
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      09
      0810106
      
      ...
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      768.038125
      2.529901e+04
      768.038125
      POLYGON ((2.110529078703358 41.35377572756204,...
    

    

      314
      5594
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      101
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      0810106
      
      ...
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      788.795447
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      788.795447
      POLYGON ((2.118425676973036 41.35454631841772,...
    

    

      315
      5595
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      09
      0810106
      
      ...
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      827.420246
      3.450004e+04
      827.420246
      POLYGON ((2.111416021000836 41.35621617853905,...
    

    

      316
      5596
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      024
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      09
      0810106
      
      ...
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      653.723365
      1.984319e+04
      653.723365
      POLYGON ((2.113550259973175 41.35606702378573,...
    

    

      317
      5597
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      025
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      09
      0810106
      
      ...
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      483.727974
      1.420906e+04
      483.727974
      POLYGON ((2.111410363308776 41.35275964605734,...
    

    

      318
      5598
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      026
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      0810106
      
      ...
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      880.786590
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      880.786590
      POLYGON ((2.117658212025399 41.35182031837279,...
    

    

      319
      5599
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      101
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      09
      0810106
      
      ...
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      1268.008353
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      POLYGON ((2.113536213566468 41.35301129383581,...
    

    

      320
      5600
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      0810106
      
      ...
      1
      1
      08101
      Barcelona
      Catalua
      Hospitalet de Llobregat, L'
      1071.470077
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      POLYGON ((2.110999495684243 41.35050065082766,...
    

    

      321
      5601
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      09
      0810106
      
      ...
      1
      1
      08101
      Barcelona
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      Hospitalet de Llobregat, L'
      760.790096
      2.088928e+04
      760.790096
      POLYGON ((2.117633170576807 41.35397195269742,...
    

    

      322
      5602
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      09
      0810106
      
      ...
      1
      1
      08101
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      ...
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      POLYGON ((2.113107605274231 41.35047178057036,...
    

    

      324
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      ...
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      1112.678450
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      POLYGON ((2.115942307489964 41.35669826603879,...
    

  



325 rows × 22 columns



















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Content source: tgquintela/temporal_tasks

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