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Graph election results





In [1]:



    


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

import matplotlib
%matplotlib inline



    











In [2]:



    


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



    











In [3]:



    


par_splits = {'n_splits': 10, 'limits': (0, 1),
              'open_limits': True, 'reverse': False}
splitter = Splitters('equispaced_splitters', par_splits, ranges=None)



    











In [4]:



    


splitter(0.5)



    


















    
Out[4]:








6

















In [5]:



    


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



    











In [6]:



    


(data_censo['CUMUN'] == '08073').sum()



    


















    
Out[6]:








70

















In [7]:



    


data_censo['CUSEC'] = data_censo['CUSEC'].apply(lambda x: str(x))



    











In [39]:



    


#pro_ind = pd.DataFrame([data_censo['CUSEC'].as_matrix(), np.random.random(len(data_censo))]).T
#pro_ind.rename(columns={0: 'CUSEC', 1: 'Pro_ind'}, inplace=True)
g = data_censo.loc[data_censo['CUDIS'] == '0807301', 'geometry'].unary_union
type(g)
data_censo.columns



    


















    
Out[39]:








Index([  u'OBJECTID',      u'CUSEC',      u'CUMUN',       u'CSEC',
             u'CDIS',       u'CMUN',       u'CPRO',        u'CCA',
            u'CUDIS',        u'OBS',      u'CNUT0',      u'CNUT1',
            u'CNUT2',      u'CNUT3',      u'CLAU2',       u'NPRO',
              u'NCA',       u'NMUN', u'Shape_Leng', u'Shape_area',
        u'Shape_len',   u'geometry'],
      dtype='object')

















In [43]:



    


class GeoDataCensal(object):

    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(isinstance(var_hierarchy['geometry'], str))
        assert(isinstance(var_hierarchy['codes'], list))
        assert(isinstance(var_hierarchy['names'], 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 = 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)
        return data

    def _filter_hierharchy_by_level(self, level):
        var_hierarchy = {}
        var_hierarchy['geometry'] = self.var_hierarchy['geometry']
        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)



    











In [58]:



    


var_hierarchy = {
    'names': ['CUSEC', 'CUDIS', 'NMUN', 'NPRO', 'NCA'],
    'codes': ['CUSEC', 'CUDIS', 'CUMUN', 'CPRO', 'CCA'],
    'geometry': 'geometry'
}
datacensal = GeoDataCensal(data_censo, var_hierarchy)

datacensal.filter_data_by_level(5, False).data_censal



    


















    
Out[58]:










  
    

      
      CUSEC
      geometry
      CPRO
      NMUN
      CUMUN
      NCA
      CCA
      CUDIS
      NPRO
    

  
  
    

      0
      0807301001
      POLYGON ((2.07086332857145 41.35740405853809, ...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807301
      Barcelona
    

    

      1
      0807301002
      POLYGON ((2.070671819595459 41.35602864763179,...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807301
      Barcelona
    

    

      2
      0807301003
      POLYGON ((2.077546728450129 41.35071454754457,...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807301
      Barcelona
    

    

      3
      0807301004
      POLYGON ((2.049558400975442 41.34744746709448,...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807301
      Barcelona
    

    

      4
      0807301005
      POLYGON ((2.076844113283871 41.35191659116927,...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807301
      Barcelona
    

    

      5
      0807301006
      POLYGON ((2.072357123758789 41.35348683736885,...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807301
      Barcelona
    

    

      6
      0807301007
      POLYGON ((2.071632524362656 41.35349974731189,...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807301
      Barcelona
    

    

      7
      0807301008
      POLYGON ((2.067353723362825 41.35395055034011,...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807301
      Barcelona
    

    

      8
      0807301009
      POLYGON ((2.073513537376484 41.35354297793097,...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807301
      Barcelona
    

    

      9
      0807301010
      POLYGON ((2.076351447228844 41.35300580311168,...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807301
      Barcelona
    

    

      10
      0807301011
      POLYGON ((2.075231238184402 41.3502418834029, ...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807301
      Barcelona
    

    

      11
      0807301012
      POLYGON ((2.078138395536048 41.34948543969318,...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807301
      Barcelona
    

    

      12
      0807301013
      POLYGON ((2.070673220012849 41.35183742555718,...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807301
      Barcelona
    

    

      13
      0807301014
      POLYGON ((2.068555991335256 41.3545286687766, ...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807301
      Barcelona
    

    

      14
      0807302001
      POLYGON ((2.093625220364088 41.3586287235924, ...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807302
      Barcelona
    

    

      15
      0807302002
      POLYGON ((2.098414563671479 41.35412837797859,...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807302
      Barcelona
    

    

      16
      0807303001
      POLYGON ((2.07611255018573 41.36475331743631, ...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807303
      Barcelona
    

    

      17
      0807303002
      POLYGON ((2.074202166513067 41.36662283360337,...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807303
      Barcelona
    

    

      18
      0807303003
      POLYGON ((2.073464007875069 41.36483626387965,...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807303
      Barcelona
    

    

      19
      0807303004
      POLYGON ((2.061162516025689 41.36492135114345,...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807303
      Barcelona
    

    

      20
      0807303005
      POLYGON ((2.070008926135653 41.36477759382425,...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807303
      Barcelona
    

    

      21
      0807303006
      POLYGON ((2.07603263954863 41.36467196132006, ...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807303
      Barcelona
    

    

      22
      0807303007
      POLYGON ((2.074968018258001 41.36358917264499,...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807303
      Barcelona
    

    

      23
      0807303008
      POLYGON ((2.063546649405958 41.35903638480863,...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807303
      Barcelona
    

    

      24
      0807303009
      POLYGON ((2.059556796436511 41.35907671405715,...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807303
      Barcelona
    

    

      25
      0807304001
      POLYGON ((2.074017914747701 41.35884374941005,...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807304
      Barcelona
    

    

      26
      0807304002
      POLYGON ((2.07694456943873 41.35708127848684, ...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807304
      Barcelona
    

    

      27
      0807304003
      POLYGON ((2.073510737054216 41.36047889484263,...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807304
      Barcelona
    

    

      28
      0807304004
      POLYGON ((2.080737635651198 41.35739498245629,...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807304
      Barcelona
    

    

      29
      0807304005
      POLYGON ((2.080048319263547 41.35832426126629,...
      08
      Cornell de Llobregat
      08073
      Catalua
      09
      0807304
      Barcelona
    

    

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

    

      295
      0810106003
      POLYGON ((2.107346478362054 41.35299296880945,...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      296
      0810106004
      POLYGON ((2.10749779200804 41.34928961216207, ...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      297
      0810106005
      POLYGON ((2.109295564176914 41.35195179616392,...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      298
      0810106006
      POLYGON ((2.108177365059811 41.34770648802841,...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      299
      0810106007
      POLYGON ((2.109390191493859 41.34787372860216,...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      300
      0810106008
      POLYGON ((2.116906950356888 41.3518333115553, ...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      301
      0810106009
      POLYGON ((2.1121606071758 41.35678368835044, 2...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      302
      0810106010
      POLYGON ((2.115694358677211 41.3550198169447, ...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      303
      0810106011
      POLYGON ((2.115942307489964 41.35669826603879,...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      304
      0810106012
      POLYGON ((2.115731249011137 41.35532242585489,...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      305
      0810106013
      POLYGON ((2.115576768962717 41.35405613070908,...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      306
      0810106014
      POLYGON ((2.115341952095644 41.3521170374316, ...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      307
      0810106015
      POLYGON ((2.114575876857076 41.35080843961178,...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      308
      0810106016
      POLYGON ((2.133252597556686 41.35985738233334,...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      309
      0810106017
      POLYGON ((2.115526284594824 41.35364240699773,...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      310
      0810106018
      POLYGON ((2.109157519956944 41.35219010358173,...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      311
      0810106019
      POLYGON ((2.111164050408914 41.35010261037827,...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      312
      0810106020
      POLYGON ((2.109694363132621 41.35353451084169,...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      313
      0810106021
      POLYGON ((2.110529078703358 41.35377572756204,...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      314
      0810106022
      POLYGON ((2.118425676973036 41.35454631841772,...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      315
      0810106023
      POLYGON ((2.111416021000836 41.35621617853905,...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      316
      0810106024
      POLYGON ((2.113550259973175 41.35606702378573,...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      317
      0810106025
      POLYGON ((2.111410363308776 41.35275964605734,...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      318
      0810106026
      POLYGON ((2.117658212025399 41.35182031837279,...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      319
      0810106027
      POLYGON ((2.113536213566468 41.35301129383581,...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      320
      0810106028
      POLYGON ((2.110999495684243 41.35050065082766,...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      321
      0810106029
      POLYGON ((2.117633170576807 41.35397195269742,...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      322
      0810106030
      POLYGON ((2.112889569395336 41.35435189716479,...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      323
      0810106031
      POLYGON ((2.113107605274231 41.35047178057036,...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

    

      324
      0810106032
      POLYGON ((2.115942307489964 41.35669826603879,...
      08
      Hospitalet de Llobregat, L'
      08101
      Catalua
      09
      0810106
      Barcelona
    

  



325 rows × 9 columns



















In [9]:



    


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 [10]:



    


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



    











In [11]:



    


votos2015



    


















    
Out[11]:










  
    

      
      CUSEC
      PSC
      PP
      C's
      PACMA
      JxSi
      CSQEP
      unio.cat
      CUP
      RECORTES0
      pirata.cat
      ganemos
    

  
  
    

      0
      0800101001
      175
      84
      201
      14
      249
      106
      18
      70
      4
      0
      0
    

    

      1
      0800101002
      219
      90
      227
      16
      187
      149
      20
      52
      6
      0
      0
    

    

      2
      0800101003
      74
      28
      114
      4
      158
      52
      7
      31
      5
      0
      0
    

    

      3
      0800101004
      146
      70
      277
      6
      298
      138
      26
      90
      9
      0
      0
    

    

      4
      0800101005
      247
      100
      246
      10
      174
      143
      18
      68
      13
      0
      0
    

    

      5
      0800101007
      223
      157
      399
      27
      363
      170
      21
      85
      5
      0
      0
    

    

      6
      0800101008
      191
      90
      307
      5
      256
      180
      21
      57
      9
      0
      0
    

    

      7
      0801401001
      108
      84
      82
      8
      1048
      83
      54
      159
      1
      0
      0
    

    

      8
      0800301001
      119
      89
      169
      2
      967
      78
      55
      136
      3
      0
      0
    

    

      9
      0800301002
      97
      164
      273
      10
      686
      84
      52
      100
      3
      0
      0
    

    

      10
      0800301003
      76
      95
      147
      10
      882
      35
      53
      121
      1
      0
      0
    

    

      11
      0800301004
      90
      133
      263
      8
      614
      67
      47
      112
      1
      0
      0
    

    

      12
      0800501001
      87
      79
      206
      7
      754
      85
      39
      102
      3
      0
      0
    

    

      13
      0800501002
      55
      52
      94
      1
      696
      45
      33
      81
      2
      0
      0
    

    

      14
      0800501003
      59
      78
      177
      11
      950
      54
      57
      117
      3
      0
      0
    

    

      15
      0800501004
      34
      34
      81
      1
      368
      44
      18
      46
      2
      0
      0
    

    

      16
      0800501005
      56
      45
      101
      3
      198
      33
      11
      33
      0
      0
      0
    

    

      17
      0800601001
      114
      106
      123
      8
      733
      74
      45
      148
      4
      0
      0
    

    

      18
      0800601002
      72
      93
      106
      5
      766
      67
      37
      147
      6
      0
      0
    

    

      19
      0800601003
      62
      51
      59
      5
      401
      32
      25
      51
      0
      0
      0
    

    

      20
      0800601004
      30
      45
      96
      4
      323
      36
      30
      63
      1
      0
      0
    

    

      21
      0800602001
      87
      68
      81
      2
      534
      67
      22
      85
      0
      0
      0
    

    

      22
      0800602002
      76
      64
      58
      4
      711
      47
      28
      119
      3
      0
      0
    

    

      23
      0800602003
      82
      90
      109
      2
      587
      40
      24
      84
      1
      0
      0
    

    

      24
      0800602004
      117
      89
      170
      7
      338
      66
      20
      63
      4
      0
      0
    

    

      25
      0800602005
      140
      81
      107
      4
      365
      64
      17
      43
      0
      0
      0
    

    

      26
      0800701001
      65
      42
      62
      4
      573
      49
      20
      76
      1
      0
      0
    

    

      27
      0800701002
      130
      88
      165
      6
      1111
      83
      33
      198
      3
      0
      0
    

    

      28
      0800702001
      67
      79
      95
      5
      874
      48
      45
      141
      4
      0
      0
    

    

      29
      0800702002
      86
      73
      135
      8
      754
      55
      33
      98
      5
      0
      0
    

    

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

    

      5018
      4315501001
      46
      38
      70
      5
      205
      22
      15
      40
      4
      0
      1
    

    

      5019
      4315501002
      41
      30
      54
      2
      186
      34
      17
      34
      0
      0
      0
    

    

      5020
      4315501003
      71
      72
      76
      4
      325
      27
      30
      40
      1
      0
      0
    

    

      5021
      4315501004
      111
      108
      149
      9
      516
      52
      35
      82
      3
      0
      0
    

    

      5022
      4315501005
      40
      45
      10
      4
      101
      17
      16
      17
      1
      0
      0
    

    

      5023
      4315501006
      45
      84
      81
      1
      238
      19
      29
      46
      0
      0
      0
    

    

      5024
      4315501007
      122
      100
      234
      9
      788
      97
      63
      117
      5
      0
      0
    

    

      5025
      4315502002
      79
      69
      116
      2
      652
      60
      39
      107
      2
      0
      0
    

    

      5026
      4315502003
      113
      43
      108
      7
      441
      45
      21
      72
      1
      0
      1
    

    

      5027
      4315502004
      58
      38
      54
      4
      220
      35
      12
      39
      1
      0
      0
    

    

      5028
      4315502005
      65
      27
      74
      0
      421
      27
      30
      44
      1
      0
      1
    

    

      5029
      4315503001
      53
      28
      36
      0
      117
      16
      5
      34
      3
      0
      0
    

    

      5030
      4315503002
      77
      49
      72
      1
      349
      39
      23
      85
      1
      0
      0
    

    

      5031
      4315503003
      102
      45
      48
      0
      252
      38
      24
      48
      0
      0
      0
    

    

      5032
      4315503004
      85
      55
      107
      2
      461
      68
      39
      87
      4
      0
      0
    

    

      5033
      4315504001
      67
      73
      107
      4
      346
      51
      31
      91
      2
      0
      1
    

    

      5034
      4315504002
      74
      61
      105
      3
      299
      37
      20
      55
      5
      0
      2
    

    

      5035
      4315504003
      89
      54
      95
      8
      292
      60
      19
      80
      2
      0
      1
    

    

      5036
      4315504004
      47
      63
      40
      1
      164
      21
      10
      27
      0
      0
      0
    

    

      5037
      4315504005
      79
      67
      122
      5
      468
      51
      22
      80
      0
      0
      0
    

    

      5038
      4315504006
      67
      56
      87
      2
      408
      49
      26
      83
      0
      0
      0
    

    

      5039
      4315504007
      91
      54
      76
      1
      264
      28
      13
      48
      0
      0
      0
    

    

      5040
      4315504008
      34
      24
      65
      2
      277
      27
      11
      50
      2
      0
      0
    

    

      5041
      4315504009
      70
      62
      130
      7
      355
      59
      26
      63
      0
      0
      2
    

    

      5042
      4315601001
      118
      157
      135
      3
      399
      39
      42
      99
      1
      0
      0
    

    

      5043
      4315601002
      133
      147
      159
      2
      596
      53
      27
      142
      0
      0
      0
    

    

      5044
      4315602001
      72
      102
      42
      3
      368
      41
      18
      68
      2
      0
      1
    

    

      5045
      4317501001
      19
      59
      26
      2
      282
      8
      34
      29
      2
      0
      1
    

    

      5046
      4317701001
      13
      12
      23
      1
      163
      8
      3
      27
      0
      0
      2
    

    

      5047
      4305201001
      65
      42
      59
      7
      472
      27
      13
      87
      0
      0
      1
    

  



5048 rows × 12 columns



















In [12]:



    


votos2015_trio_ll, codes_not_in_censo, codes_not_votos2015 =\
    left_filter_function(data_censo, votos2015, filter_code='CUSEC')

codes_not_in_censo, codes_not_votos2015



    


















    
Out[12]:








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 {'0810102003',
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  '0810106004',
  '0810106020',
  '0810106028',
  '0810106029'})

















In [13]:



    


datainfo2015_trio_ll, codes_not_in_censo, codes_not_datainfo2015 =\
    left_filter_function(data_censo, data_info2015, filter_code='CUSEC')
codes_not_in_censo, codes_not_datainfo2015



    


















    






/usr/local/lib/python2.7/dist-packages/pandas/indexes/base.py:2286: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
  indexer = self._engine.get_indexer(target._values)
/usr/local/lib/python2.7/dist-packages/pandas/indexes/base.py:1396: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
  return key in self._engine










    
Out[13]:








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In [8]:



    


elections2015 = '/home/tono/code/Eleccions2017Cat/data/A20151_ME/Columnes_A20151_ME_ca_ES.csv'

converters = {}
data2015 = pd.read_csv(elections2015, sep=';', converters=converters)

columns = ['Codi Província', 'Nom Província', 'Codi Vegueria', 'Nom Vegueria',
           'Codi Comarca', 'Nom Comarca', 'Codi Municipi', 'Nom Municipi',
           'Districte', 'Secció', 'Mesa', 'Cens electoral',
           'Participació 13:00', 'Participació 18.00', 'Participació 20.00',
           'Abstenció', 'Vots nuls', 'Vots en blanc', 'Vots a candidatures',
           'Vots vàlids']
n_cols = len(columns)
cols_new = [str(c) for c in data2015.columns]
data2015.columns = cols_new


votos = data2015.columns[len(columns):][[2*i for i in range((len(data2015.columns)-len(columns))//2)]]
parties = ["PSC", "PP", "C's", "PACMA", "JxSi", "CSQEP", "unio.cat", "CUP", "RECORTES0", "pirata.cat", "ganemos"]
votos_data2015 = data2015[votos]
votos_data2015.columns = parties

#data2015[data2015.columns[:20]]

CUSEC =\
    [str(data2015.iloc[i, 6]).zfill(5)+str(data2015.iloc[i, 8]).zfill(2)+str(data2015.iloc[i, 9]).zfill(3)
     for i in range(len(data2015))]
CUSEC2015 = pd.DataFrame(pd.Series(CUSEC), columns=['CUSEC'])

votos2015 = pd.concat([CUSEC2015, votos_data2015], axis=1)



    











In [ ]:



    


for cusec in votos2015['CUSEC']:
    if cusec not in list(data_censo['CUSEC']):
        print(cusec)



    


















    






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0828402003
















In [9]:



    


## Votos 2015
####################
df = []
u_cusec = votos2015['CUSEC'].unique()
for cusec_i in u_cusec:
    df.append(votos2015.loc[(votos2015['CUSEC'] == cusec_i), :].iloc[:, 1:].sum(0))
votos2015 = pd.concat([pd.DataFrame(u_cusec, columns=['CUSEC']), pd.DataFrame(df)], axis=1)



    











In [23]:



    


data2015.loc[data2015['Codi Municipi'] == 8073]



    


















    
Out[23]:










  
    

      
      Codi Prov�ncia
      Nom Prov�ncia
      Codi Vegueria
      Nom Vegueria
      Codi Comarca
      Nom Comarca
      Codi Municipi
      Nom Municipi
      Districte
      Secci�
      ...
      unio.cat Vots
      unio.cat Diputats
      CUP Vots
      CUP Diputats
      RECORTES 0-EV Vots
      RECORTES 0-EV Diputats
      PIRATA.CAT/XDT Vots
      PIRATA.CAT/XDT Diputats
      GANEMOS Vots
      GANEMOS Diputats
    

  
  
    

      2036
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      1
      1
      ...
      21
      0
      76
      0
      0
      0
      0
      0
      0
      0
    

    

      2037
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      1
      1
      ...
      10
      0
      69
      0
      3
      0
      0
      0
      0
      0
    

    

      2038
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      1
      10
      ...
      7
      0
      27
      0
      1
      0
      0
      0
      0
      0
    

    

      2039
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      1
      10
      ...
      8
      0
      41
      0
      2
      0
      0
      0
      0
      0
    

    

      2040
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      1
      11
      ...
      4
      0
      25
      0
      0
      0
      0
      0
      0
      0
    

    

      2041
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      1
      11
      ...
      4
      0
      24
      0
      1
      0
      0
      0
      0
      0
    

    

      2042
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      1
      12
      ...
      5
      0
      38
      0
      2
      0
      0
      0
      0
      0
    

    

      2043
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      1
      13
      ...
      11
      0
      29
      0
      5
      0
      0
      0
      0
      0
    

    

      2044
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      1
      13
      ...
      12
      0
      30
      0
      3
      0
      0
      0
      0
      0
    

    

      2045
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      1
      14
      ...
      12
      0
      83
      0
      2
      0
      0
      0
      0
      0
    

    

      2046
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      1
      2
      ...
      10
      0
      50
      0
      0
      0
      0
      0
      0
      0
    

    

      2047
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      1
      2
      ...
      7
      0
      42
      0
      1
      0
      0
      0
      0
      0
    

    

      2048
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      1
      3
      ...
      3
      0
      20
      0
      4
      0
      0
      0
      0
      0
    

    

      2049
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      1
      3
      ...
      7
      0
      31
      0
      2
      0
      0
      0
      0
      0
    

    

      2050
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      1
      4
      ...
      9
      0
      36
      0
      5
      0
      0
      0
      0
      0
    

    

      2051
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      1
      4
      ...
      10
      0
      45
      0
      7
      0
      0
      0
      0
      0
    

    

      2052
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      1
      5
      ...
      16
      0
      60
      0
      5
      0
      0
      0
      0
      0
    

    

      2053
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      1
      6
      ...
      9
      0
      55
      0
      3
      0
      0
      0
      0
      0
    

    

      2054
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      1
      7
      ...
      11
      0
      62
      0
      2
      0
      0
      0
      0
      0
    

    

      2055
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      1
      7
      ...
      18
      0
      78
      0
      4
      0
      0
      0
      0
      0
    

    

      2056
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      1
      8
      ...
      8
      0
      50
      0
      3
      0
      0
      0
      0
      0
    

    

      2057
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      1
      8
      ...
      3
      0
      53
      0
      6
      0
      0
      0
      0
      0
    

    

      2058
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      1
      9
      ...
      5
      0
      29
      0
      2
      0
      0
      0
      0
      0
    

    

      2059
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      1
      9
      ...
      2
      0
      31
      0
      3
      0
      0
      0
      0
      0
    

    

      2060
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      2
      1
      ...
      15
      0
      47
      0
      2
      0
      0
      0
      0
      0
    

    

      2061
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      2
      1
      ...
      11
      0
      39
      0
      3
      0
      0
      0
      0
      0
    

    

      2062
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      2
      1
      ...
      22
      0
      48
      0
      0
      0
      0
      0
      0
      0
    

    

      2063
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      2
      2
      ...
      8
      0
      24
      0
      5
      0
      0
      0
      0
      0
    

    

      2064
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      2
      2
      ...
      12
      0
      24
      0
      3
      0
      0
      0
      0
      0
    

    

      2065
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      2
      2
      ...
      11
      0
      31
      0
      4
      0
      0
      0
      0
      0
    

    

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

    

      2121
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      5
      3
      ...
      1
      0
      8
      0
      2
      0
      0
      0
      0
      0
    

    

      2122
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      5
      4
      ...
      6
      0
      10
      0
      6
      0
      0
      0
      0
      0
    

    

      2123
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      5
      5
      ...
      5
      0
      15
      0
      5
      0
      0
      0
      0
      0
    

    

      2124
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      5
      6
      ...
      4
      0
      5
      0
      1
      0
      0
      0
      0
      0
    

    

      2125
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      5
      6
      ...
      2
      0
      18
      0
      3
      0
      0
      0
      0
      0
    

    

      2126
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      5
      7
      ...
      1
      0
      6
      0
      1
      0
      0
      0
      0
      0
    

    

      2127
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      5
      7
      ...
      1
      0
      8
      0
      3
      0
      0
      0
      0
      0
    

    

      2128
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      5
      8
      ...
      1
      0
      4
      0
      1
      0
      0
      0
      0
      0
    

    

      2129
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      5
      8
      ...
      2
      0
      9
      0
      6
      0
      0
      0
      0
      0
    

    

      2130
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      5
      9
      ...
      1
      0
      30
      0
      6
      0
      0
      0
      0
      0
    

    

      2131
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      6
      1
      ...
      2
      0
      10
      0
      1
      0
      0
      0
      0
      0
    

    

      2132
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      6
      10
      ...
      1
      0
      9
      0
      3
      0
      0
      0
      0
      0
    

    

      2133
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      6
      11
      ...
      2
      0
      13
      0
      1
      0
      0
      0
      0
      0
    

    

      2134
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      6
      12
      ...
      7
      0
      9
      0
      2
      0
      0
      0
      0
      0
    

    

      2135
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      6
      13
      ...
      4
      0
      10
      0
      4
      0
      0
      0
      0
      0
    

    

      2136
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      6
      14
      ...
      3
      0
      10
      0
      4
      0
      0
      0
      0
      0
    

    

      2137
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      6
      15
      ...
      4
      0
      21
      0
      6
      0
      0
      0
      0
      0
    

    

      2138
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      6
      16
      ...
      7
      0
      5
      0
      5
      0
      0
      0
      0
      0
    

    

      2139
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      6
      2
      ...
      0
      0
      5
      0
      2
      0
      0
      0
      0
      0
    

    

      2140
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      6
      2
      ...
      2
      0
      4
      0
      3
      0
      0
      0
      0
      0
    

    

      2141
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      6
      3
      ...
      3
      0
      5
      0
      2
      0
      0
      0
      0
      0
    

    

      2142
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      6
      4
      ...
      0
      0
      10
      0
      1
      0
      0
      0
      0
      0
    

    

      2143
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      6
      5
      ...
      3
      0
      9
      0
      0
      0
      0
      0
      0
      0
    

    

      2144
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      6
      6
      ...
      6
      0
      11
      0
      3
      0
      0
      0
      0
      0
    

    

      2145
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      6
      7
      ...
      6
      0
      1
      0
      1
      0
      0
      0
      0
      0
    

    

      2146
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      6
      7
      ...
      3
      0
      14
      0
      0
      0
      0
      0
      0
      0
    

    

      2147
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      6
      8
      ...
      4
      0
      6
      0
      5
      0
      0
      0
      0
      0
    

    

      2148
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      6
      9
      ...
      3
      0
      12
      0
      1
      0
      0
      0
      0
      0
    

    

      2149
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      6
      9
      ...
      3
      0
      8
      0
      1
      0
      0
      0
      0
      0
    

    

      2150
      8
      Barcelona
      1
      Barcelona
      11
      Baix Llobregat
      8073
      Cornell� de Llobregat
      6
      9
      ...
      5
      0
      8
      0
      0
      0
      0
      0
      0
      0
    

  



115 rows × 42 columns



















In [13]:



    


df = []
u_cusec = votos2015['CUSEC'].unique()
for cusec_i in u_cusec:
    df.append(data2015.iloc[:, 19][(data2015['CUSEC'] == cusec_i)].sum(0))
summary_ind_2015 =\
    pd.concat([pd.DataFrame(u_cusec, columns=['CUSEC']),
               pd.DataFrame(pd.Series(df), columns=['VotosValidos']),
               pd.DataFrame(votos2015[["JxSi", "CUP"]].sum(1), columns=['Pro_ind_votos'])],
              axis=1)
summary_ind_2015['Pro_ind'] =\
    summary_ind_2015['Pro_ind_votos']/summary_ind_2015['VotosValidos']



    


















    






---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-13-739e3630466a> in <module>()
      2 u_cusec = votos2015['CUSEC'].unique()
      3 for cusec_i in u_cusec:
----> 4     df.append(data2015.iloc[:, 19][(data2015['CUSEC'] == cusec_i)].sum(0))
      5 summary_ind_2015 =    pd.concat([pd.DataFrame(u_cusec, columns=['CUSEC']),
      6                pd.DataFrame(pd.Series(df), columns=['VotosValidos']),

/usr/local/lib/python2.7/dist-packages/pandas/core/frame.pyc in __getitem__(self, key)
   2057             return self._getitem_multilevel(key)
   2058         else:
-> 2059             return self._getitem_column(key)
   2060 
   2061     def _getitem_column(self, key):

/usr/local/lib/python2.7/dist-packages/pandas/core/frame.pyc in _getitem_column(self, key)
   2064         # get column
   2065         if self.columns.is_unique:
-> 2066             return self._get_item_cache(key)
   2067 
   2068         # duplicate columns & possible reduce dimensionality

/usr/local/lib/python2.7/dist-packages/pandas/core/generic.pyc in _get_item_cache(self, item)
   1384         res = cache.get(item)
   1385         if res is None:
-> 1386             values = self._data.get(item)
   1387             res = self._box_item_values(item, values)
   1388             cache[item] = res

/usr/local/lib/python2.7/dist-packages/pandas/core/internals.pyc in get(self, item, fastpath)
   3541 
   3542             if not isnull(item):
-> 3543                 loc = self.items.get_loc(item)
   3544             else:
   3545                 indexer = np.arange(len(self.items))[isnull(self.items)]

/usr/local/lib/python2.7/dist-packages/pandas/indexes/base.pyc in get_loc(self, key, method, tolerance)
   2134                 return self._engine.get_loc(key)
   2135             except KeyError:
-> 2136                 return self._engine.get_loc(self._maybe_cast_indexer(key))
   2137 
   2138         indexer = self.get_indexer([key], method=method, tolerance=tolerance)

pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4433)()

pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4279)()

pandas/src/hashtable_class_helper.pxi in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13742)()

pandas/src/hashtable_class_helper.pxi in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13696)()

KeyError: 'CUSEC'
















In [ ]:



    


summary_ind_2015_trio = pd.merge(data_censo[['CUSEC']], summary_ind_2015, how='left')



    











In [292]:



    


summary_ind_2015_trio[pd.isnull(summary_ind_2015_trio).any(1)]



    


















    
Out[292]:










  
    

      
      CUSEC
      VotosValidos
      Pro_ind_votos
      Pro_ind
    

  
  
    

      141
      0810102003
      NaN
      NaN
      NaN
    

    

      144
      0810102006
      NaN
      NaN
      NaN
    

    

      153
      0810102015
      NaN
      NaN
      NaN
    

    

      156
      0810102018
      NaN
      NaN
      NaN
    

    

      157
      0810102019
      NaN
      NaN
      NaN
    

    

      160
      0810102022
      NaN
      NaN
      NaN
    

    

      163
      0810102025
      NaN
      NaN
      NaN
    

    

      165
      0810102027
      NaN
      NaN
      NaN
    

    

      168
      0810102030
      NaN
      NaN
      NaN
    

    

      173
      0810102035
      NaN
      NaN
      NaN
    

    

      179
      0810102041
      NaN
      NaN
      NaN
    

    

      182
      0810102044
      NaN
      NaN
      NaN
    

    

      220
      0810104008
      NaN
      NaN
      NaN
    

    

      225
      0810104013
      NaN
      NaN
      NaN
    

    

      227
      0810104015
      NaN
      NaN
      NaN
    

    

      229
      0810104017
      NaN
      NaN
      NaN
    

    

      236
      0810104024
      NaN
      NaN
      NaN
    

    

      240
      0810104028
      NaN
      NaN
      NaN
    

    

      241
      0810104029
      NaN
      NaN
      NaN
    

    

      246
      0810104034
      NaN
      NaN
      NaN
    

    

      250
      0810104038
      NaN
      NaN
      NaN
    

    

      251
      0810104039
      NaN
      NaN
      NaN
    

    

      254
      0810104042
      NaN
      NaN
      NaN
    

    

      263
      0810105009
      NaN
      NaN
      NaN
    

    

      266
      0810105012
      NaN
      NaN
      NaN
    

    

      269
      0810105015
      NaN
      NaN
      NaN
    

    

      270
      0810105016
      NaN
      NaN
      NaN
    

    

      273
      0810105019
      NaN
      NaN
      NaN
    

    

      276
      0810105022
      NaN
      NaN
      NaN
    

    

      281
      0810105027
      NaN
      NaN
      NaN
    

    

      290
      0810105036
      NaN
      NaN
      NaN
    

    

      295
      0810106003
      NaN
      NaN
      NaN
    

    

      296
      0810106004
      NaN
      NaN
      NaN
    

    

      312
      0810106020
      NaN
      NaN
      NaN
    

    

      320
      0810106028
      NaN
      NaN
      NaN
    

    

      321
      0810106029
      NaN
      NaN
      NaN
    

  




















In [83]:



    


m = folium.Map([41.37, 2.1], tiles='cartodbpositron', zoom_start=13)

m.choropleth(
    geo_data=file_geojson,
    data=pro_ind,
    columns=['CUSEC', 'Pro_ind'],
    key_on='feature.properties.CUSEC',
    fill_color='YlGn',
    highlight=True,
    )
m



    


















    
Out[83]:
























In [67]:



    


help(m.choropleth)



    


















    






Help on method choropleth in module folium.folium:

choropleth(self, geo_data, data=None, columns=None, key_on=None, threshold_scale=None, fill_color='blue', fill_opacity=0.6, line_color='black', line_weight=1, line_opacity=1, name=None, legend_name='', topojson=None, reset=False, smooth_factor=None, highlight=None) method of folium.folium.Map instance
    Apply a GeoJSON overlay to the map.
    
    Plot a GeoJSON overlay on the base map. There is no requirement
    to bind data (passing just a GeoJSON plots a single-color overlay),
    but there is a data binding option to map your columnar data to
    different feature objects with a color scale.
    
    If data is passed as a Pandas DataFrame, the "columns" and "key-on"
    keywords must be included, the first to indicate which DataFrame
    columns to use, the second to indicate the layer in the GeoJSON
    on which to key the data. The 'columns' keyword does not need to be
    passed for a Pandas series.
    
    Colors are generated from color brewer (http://colorbrewer2.org/)
    sequential palettes on a D3 threshold scale. The scale defaults to the
    following quantiles: [0, 0.5, 0.75, 0.85, 0.9]. A custom scale can be
    passed to `threshold_scale` of length <=6, in order to match the
    color brewer range.
    
    TopoJSONs can be passed as "geo_data", but the "topojson" keyword must
    also be passed with the reference to the topojson objects to convert.
    See the topojson.feature method in the TopoJSON API reference:
    https://github.com/topojson/topojson/wiki/API-Reference
    
    
    Parameters
    ----------
    geo_data: string/object
        URL, file path, or data (json, dict, geopandas, etc) to your GeoJSON geometries
    data: Pandas DataFrame or Series, default None
        Data to bind to the GeoJSON.
    columns: dict or tuple, default None
        If the data is a Pandas DataFrame, the columns of data to be bound.
        Must pass column 1 as the key, and column 2 the values.
    key_on: string, default None
        Variable in the GeoJSON file to bind the data to. Must always
        start with 'feature' and be in JavaScript objection notation.
        Ex: 'feature.id' or 'feature.properties.statename'.
    threshold_scale: list, default None
        Data range for D3 threshold scale. Defaults to the following range
        of quantiles: [0, 0.5, 0.75, 0.85, 0.9], rounded to the nearest
        order-of-magnitude integer. Ex: 270 rounds to 200, 5600 to 6000.
    fill_color: string, default 'blue'
        Area fill color. Can pass a hex code, color name, or if you are
        binding data, one of the following color brewer palettes:
        'BuGn', 'BuPu', 'GnBu', 'OrRd', 'PuBu', 'PuBuGn', 'PuRd', 'RdPu',
        'YlGn', 'YlGnBu', 'YlOrBr', and 'YlOrRd'.
    fill_opacity: float, default 0.6
        Area fill opacity, range 0-1.
    line_color: string, default 'black'
        GeoJSON geopath line color.
    line_weight: int, default 1
        GeoJSON geopath line weight.
    line_opacity: float, default 1
        GeoJSON geopath line opacity, range 0-1.
    legend_name: string, default empty string
        Title for data legend.
    topojson: string, default None
        If using a TopoJSON, passing "objects.yourfeature" to the topojson
        keyword argument will enable conversion to GeoJSON.
    reset: boolean, default False
        Remove all current geoJSON layers, start with new layer
    smooth_factor: float, default None
        How much to simplify the polyline on each zoom level. More means
        better performance and smoother look, and less means more accurate
        representation. Leaflet defaults to 1.0.
    highlight: boolean, default False
        Enable highlight functionality when hovering over a GeoJSON area.
    
    Returns
    -------
    GeoJSON data layer in obj.template_vars
    
    Examples
    --------
    >>> m.choropleth(geo_data='us-states.json', line_color='blue',
    ...              line_weight=3)
    >>> m.choropleth(geo_data='geo.json', data=df,
    ...              columns=['Data 1', 'Data 2'],
    ...              key_on='feature.properties.myvalue',
    ...              fill_color='PuBu',
    ...              threshold_scale=[0, 20, 30, 40, 50, 60])
    >>> m.choropleth(geo_data='countries.json',
    ...              topojson='objects.countries')
    >>> m.choropleth(geo_data='geo.json', data=df,
    ...              columns=['Data 1', 'Data 2'],
    ...              key_on='feature.properties.myvalue',
    ...              fill_color='PuBu',
    ...              threshold_scale=[0, 20, 30, 40, 50, 60],
    ...              highlight=True)


















In [72]:



    


help(m.add_child)



    


















    






Help on method add_to in module branca.element:

add_to(self, parent, name=None, index=None) method of folium.folium.Map instance
    Add element to a parent.


















In [89]:



    


#m._children['geo_json_79b9e4e8b7304fa59ba9b0397992e40a']
#m._children['color_map_15da5ba0b18b4e248a9a7c24dd9fad04']
a = m._children['geo_json_79b9e4e8b7304fa59ba9b0397992e40a']



    


















    
Out[89]:








OrderedDict()

















In [76]:



    


m = folium.Map([41.37, 2.1], tiles='cartodbpositron', zoom_start=13)

folium.RegularPolygonMarker
folium.



    











In [99]:



    


m = folium.Map([41.37, 2.1], tiles='cartodbpositron', zoom_start=13)

geo_cens = folium.folium.GeoJson(file_geojson, style_function=None,
                                 highlight_function=None, overlay=True, control=False)
geo_cens.add_to(m, 'regiones_censales')
m



    


















    
Out[99]:
























In [97]:



    


help(folium.folium.GeoJson)



    


















    






Help on class GeoJson in module folium.features:

class GeoJson(folium.map.Layer)
 |  Creates a GeoJson object for plotting into a Map.
 |  
 |  Parameters
 |  ----------
 |  data: file, dict or str.
 |      The GeoJSON data you want to plot.
 |      * If file, then data will be read in the file and fully
 |      embedded in Leaflet's JavaScript.
 |      * If dict, then data will be converted to JSON and embedded
 |      in the JavaScript.
 |      * If str, then data will be passed to the JavaScript as-is.
 |  style_function: function, default None
 |      Function mapping a GeoJson Feature to a style dict.
 |  highlight_function: function, default None
 |      Function mapping a GeoJson Feature to a style dict for mouse events.
 |  name : string, default None
 |      The name of the Layer, as it will appear in LayerControls
 |  overlay : bool, default False
 |      Adds the layer as an optional overlay (True) or the base layer (False).
 |  control : bool, default True
 |      Whether the Layer will be included in LayerControls
 |  smooth_factor: float, default None
 |      How much to simplify the polyline on each zoom level. More means
 |      better performance and smoother look, and less means more accurate
 |      representation. Leaflet defaults to 1.0.
 |  
 |  Examples
 |  --------
 |  >>> # Providing file that shall be embedded.
 |  >>> GeoJson(open('foo.json'))
 |  >>> # Providing filename that shall not be embedded.
 |  >>> GeoJson('foo.json')
 |  >>> # Providing dict.
 |  >>> GeoJson(json.load(open('foo.json')))
 |  >>> # Providing string.
 |  >>> GeoJson(open('foo.json').read())
 |  
 |  >>> # Provide a style_function that color all states green but Alabama.
 |  >>> style_function = lambda x: {'fillColor': '#0000ff' if
 |  ...                             x['properties']['name']=='Alabama' else
 |  ...                             '#00ff00'}
 |  >>> GeoJson(geojson, style_function=style_function)
 |  
 |  Method resolution order:
 |      GeoJson
 |      folium.map.Layer
 |      branca.element.MacroElement
 |      branca.element.Element
 |      __builtin__.object
 |  
 |  Methods defined here:
 |  
 |  __init__(self, data, style_function=None, name=None, overlay=True, control=True, smooth_factor=None, highlight_function=None)
 |  
 |  style_data(self)
 |      Applies `self.style_function` to each feature of `self.data` and
 |      returns a corresponding JSON output.
 |  
 |  ----------------------------------------------------------------------
 |  Methods inherited from branca.element.MacroElement:
 |  
 |  render(self, **kwargs)
 |      Renders the HTML representation of the element.
 |  
 |  ----------------------------------------------------------------------
 |  Methods inherited from branca.element.Element:
 |  
 |  add_child(self, child, name=None, index=None)
 |      Add a child.
 |  
 |  add_children(self, child, name=None, index=None)
 |      Add a child.
 |  
 |  add_to(self, parent, name=None, index=None)
 |      Add element to a parent.
 |  
 |  get_bounds(self)
 |      Computes the bounds of the object and all it's children
 |      in the form [[lat_min, lon_min], [lat_max, lon_max]].
 |  
 |  get_name(self)
 |      Returns a string representation of the object.
 |      This string has to be unique and to be a python and
 |      javascript-compatible
 |      variable name.
 |  
 |  get_root(self)
 |      Returns the root of the elements tree.
 |  
 |  save(self, outfile, close_file=True, **kwargs)
 |      Saves an Element into a file.
 |      
 |      Parameters
 |      ----------
 |      outfile : str or file object
 |          The file (or filename) where you want to output the html.
 |      close_file : bool, default True
 |          Whether the file has to be closed after write.
 |  
 |  to_dict(self, depth=-1, ordered=True, **kwargs)
 |      Returns a dict representation of the object.
 |  
 |  to_json(self, depth=-1, **kwargs)
 |      Returns a JSON representation of the object.
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from branca.element.Element:
 |  
 |  __dict__
 |      dictionary for instance variables (if defined)
 |  
 |  __weakref__
 |      list of weak references to the object (if defined)


















In [100]:



    


help(m)



    


















    






Help on Map in module folium.folium object:

class Map(folium.map.LegacyMap)
 |  Create a Map with Folium and Leaflet.js
 |  
 |  Generate a base map of given width and height with either default
 |  tilesets or a custom tileset URL. The following tilesets are built-in
 |  to Folium. Pass any of the following to the "tiles" keyword:
 |  
 |      - "OpenStreetMap"
 |      - "Mapbox Bright" (Limited levels of zoom for free tiles)
 |      - "Mapbox Control Room" (Limited levels of zoom for free tiles)
 |      - "Stamen" (Terrain, Toner, and Watercolor)
 |      - "Cloudmade" (Must pass API key)
 |      - "Mapbox" (Must pass API key)
 |      - "CartoDB" (positron and dark_matter)
 |  
 |  You can pass a custom tileset to Folium by passing a Leaflet-style
 |  URL to the tiles parameter: ``http://{s}.yourtiles.com/{z}/{x}/{y}.png``
 |  
 |  Parameters
 |  ----------
 |  location: tuple or list, default None
 |      Latitude and Longitude of Map (Northing, Easting).
 |  width: pixel int or percentage string (default: '100%')
 |      Width of the map.
 |  height: pixel int or percentage string (default: '100%')
 |      Height of the map.
 |  tiles: str, default 'OpenStreetMap'
 |      Map tileset to use. Can choose from a list of built-in tiles,
 |      pass a custom URL or pass `None` to create a map without tiles.
 |  API_key: str, default None
 |      API key for Cloudmade or Mapbox tiles.
 |  max_zoom: int, default 18
 |      Maximum zoom depth for the map.
 |  zoom_start: int, default 10
 |      Initial zoom level for the map.
 |  attr: string, default None
 |      Map tile attribution; only required if passing custom tile URL.
 |  detect_retina: bool, default False
 |      If true and user is on a retina display, it will request four
 |      tiles of half the specified size and a bigger zoom level in place
 |      of one to utilize the high resolution.
 |  crs : str, default 'EPSG3857'
 |      Defines coordinate reference systems for projecting geographical points
 |      into pixel (screen) coordinates and back.
 |      You can use Leaflet's values :
 |      * EPSG3857 : The most common CRS for online maps, used by almost all
 |      free and commercial tile providers. Uses Spherical Mercator projection.
 |      Set in by default in Map's crs option.
 |      * EPSG4326 : A common CRS among GIS enthusiasts.
 |      Uses simple Equirectangular projection.
 |      * EPSG3395 : Rarely used by some commercial tile providers.
 |      Uses Elliptical Mercator projection.
 |      * Simple : A simple CRS that maps longitude and latitude into
 |      x and y directly. May be used for maps of flat surfaces
 |      (e.g. game maps). Note that the y axis should still be inverted
 |      (going from bottom to top).
 |  control_scale : bool, default False
 |      Whether to add a control scale on the map.
 |  prefer_canvas : bool, default False
 |      Forces Leaflet to use the Canvas back-end (if available) for
 |      vector layers instead of SVG. This can increase performance
 |      considerably in some cases (e.g. many thousands of circle
 |      markers on the map).
 |  no_touch : bool, default False
 |      Forces Leaflet to not use touch events even if it detects them.
 |  disable_3d : bool, default False
 |      Forces Leaflet to not use hardware-accelerated CSS 3D
 |      transforms for positioning (which may cause glitches in some
 |      rare environments) even if they're supported.
 |  
 |  Returns
 |  -------
 |  Folium LegacyMap Object
 |  
 |  Examples
 |  --------
 |  >>> map = folium.LegacyMap(location=[45.523, -122.675],
 |  ...                        width=750, height=500)
 |  >>> map = folium.LegacyMap(location=[45.523, -122.675],
 |                             tiles='Mapbox Control Room')
 |  >>> map = folium.LegacyMap(location=(45.523, -122.675), max_zoom=20,
 |                             tiles='Cloudmade', API_key='YourKey')
 |  >>> map = folium.LegacyMap(
 |  ...    location=[45.523, -122.675],
 |  ...    zoom_start=2,
 |  ...    tiles='http://{s}.tiles.mapbox.com/v3/mapbox.control-room/{z}/{x}/{y}.png',
 |  ...    attr='Mapbox attribution'
 |  ...)
 |  
 |  Method resolution order:
 |      Map
 |      folium.map.LegacyMap
 |      branca.element.MacroElement
 |      branca.element.Element
 |      __builtin__.object
 |  
 |  Methods defined here:
 |  
 |  choropleth(self, geo_data, data=None, columns=None, key_on=None, threshold_scale=None, fill_color='blue', fill_opacity=0.6, line_color='black', line_weight=1, line_opacity=1, name=None, legend_name='', topojson=None, reset=False, smooth_factor=None, highlight=None)
 |      Apply a GeoJSON overlay to the map.
 |      
 |      Plot a GeoJSON overlay on the base map. There is no requirement
 |      to bind data (passing just a GeoJSON plots a single-color overlay),
 |      but there is a data binding option to map your columnar data to
 |      different feature objects with a color scale.
 |      
 |      If data is passed as a Pandas DataFrame, the "columns" and "key-on"
 |      keywords must be included, the first to indicate which DataFrame
 |      columns to use, the second to indicate the layer in the GeoJSON
 |      on which to key the data. The 'columns' keyword does not need to be
 |      passed for a Pandas series.
 |      
 |      Colors are generated from color brewer (http://colorbrewer2.org/)
 |      sequential palettes on a D3 threshold scale. The scale defaults to the
 |      following quantiles: [0, 0.5, 0.75, 0.85, 0.9]. A custom scale can be
 |      passed to `threshold_scale` of length <=6, in order to match the
 |      color brewer range.
 |      
 |      TopoJSONs can be passed as "geo_data", but the "topojson" keyword must
 |      also be passed with the reference to the topojson objects to convert.
 |      See the topojson.feature method in the TopoJSON API reference:
 |      https://github.com/topojson/topojson/wiki/API-Reference
 |      
 |      
 |      Parameters
 |      ----------
 |      geo_data: string/object
 |          URL, file path, or data (json, dict, geopandas, etc) to your GeoJSON geometries
 |      data: Pandas DataFrame or Series, default None
 |          Data to bind to the GeoJSON.
 |      columns: dict or tuple, default None
 |          If the data is a Pandas DataFrame, the columns of data to be bound.
 |          Must pass column 1 as the key, and column 2 the values.
 |      key_on: string, default None
 |          Variable in the GeoJSON file to bind the data to. Must always
 |          start with 'feature' and be in JavaScript objection notation.
 |          Ex: 'feature.id' or 'feature.properties.statename'.
 |      threshold_scale: list, default None
 |          Data range for D3 threshold scale. Defaults to the following range
 |          of quantiles: [0, 0.5, 0.75, 0.85, 0.9], rounded to the nearest
 |          order-of-magnitude integer. Ex: 270 rounds to 200, 5600 to 6000.
 |      fill_color: string, default 'blue'
 |          Area fill color. Can pass a hex code, color name, or if you are
 |          binding data, one of the following color brewer palettes:
 |          'BuGn', 'BuPu', 'GnBu', 'OrRd', 'PuBu', 'PuBuGn', 'PuRd', 'RdPu',
 |          'YlGn', 'YlGnBu', 'YlOrBr', and 'YlOrRd'.
 |      fill_opacity: float, default 0.6
 |          Area fill opacity, range 0-1.
 |      line_color: string, default 'black'
 |          GeoJSON geopath line color.
 |      line_weight: int, default 1
 |          GeoJSON geopath line weight.
 |      line_opacity: float, default 1
 |          GeoJSON geopath line opacity, range 0-1.
 |      legend_name: string, default empty string
 |          Title for data legend.
 |      topojson: string, default None
 |          If using a TopoJSON, passing "objects.yourfeature" to the topojson
 |          keyword argument will enable conversion to GeoJSON.
 |      reset: boolean, default False
 |          Remove all current geoJSON layers, start with new layer
 |      smooth_factor: float, default None
 |          How much to simplify the polyline on each zoom level. More means
 |          better performance and smoother look, and less means more accurate
 |          representation. Leaflet defaults to 1.0.
 |      highlight: boolean, default False
 |          Enable highlight functionality when hovering over a GeoJSON area.
 |      
 |      Returns
 |      -------
 |      GeoJSON data layer in obj.template_vars
 |      
 |      Examples
 |      --------
 |      >>> m.choropleth(geo_data='us-states.json', line_color='blue',
 |      ...              line_weight=3)
 |      >>> m.choropleth(geo_data='geo.json', data=df,
 |      ...              columns=['Data 1', 'Data 2'],
 |      ...              key_on='feature.properties.myvalue',
 |      ...              fill_color='PuBu',
 |      ...              threshold_scale=[0, 20, 30, 40, 50, 60])
 |      >>> m.choropleth(geo_data='countries.json',
 |      ...              topojson='objects.countries')
 |      >>> m.choropleth(geo_data='geo.json', data=df,
 |      ...              columns=['Data 1', 'Data 2'],
 |      ...              key_on='feature.properties.myvalue',
 |      ...              fill_color='PuBu',
 |      ...              threshold_scale=[0, 20, 30, 40, 50, 60],
 |      ...              highlight=True)
 |  
 |  fit_bounds(self, bounds, padding_top_left=None, padding_bottom_right=None, padding=None, max_zoom=None)
 |      Fit the map to contain a bounding box with the
 |      maximum zoom level possible.
 |      
 |      Parameters
 |      ----------
 |      bounds: list of (latitude, longitude) points
 |          Bounding box specified as two points [southwest, northeast]
 |      padding_top_left: (x, y) point, default None
 |          Padding in the top left corner. Useful if some elements in
 |          the corner, such as controls, might obscure objects you're zooming
 |          to.
 |      padding_bottom_right: (x, y) point, default None
 |          Padding in the bottom right corner.
 |      padding: (x, y) point, default None
 |          Equivalent to setting both top left and bottom right padding to
 |          the same value.
 |      max_zoom: int, default None
 |          Maximum zoom to be used.
 |      
 |      Examples
 |      --------
 |      >>> map.fit_bounds([[52.193636, -2.221575], [52.636878, -1.139759]])
 |  
 |  ----------------------------------------------------------------------
 |  Methods inherited from folium.map.LegacyMap:
 |  
 |  __init__(self, location=None, width='100%', height='100%', left='0%', top='0%', position='relative', tiles='OpenStreetMap', API_key=None, max_zoom=18, min_zoom=1, zoom_start=10, world_copy_jump=False, no_wrap=False, attr=None, min_lat=-90, max_lat=90, min_lon=-180, max_lon=180, max_bounds=False, detect_retina=False, crs='EPSG3857', control_scale=False, prefer_canvas=False, no_touch=False, disable_3d=False, subdomains='abc', png_enabled=False)
 |  
 |  add_tile_layer(self, tiles='OpenStreetMap', name=None, API_key=None, max_zoom=18, min_zoom=1, attr=None, active=False, detect_retina=False, no_wrap=False, subdomains='abc', **kwargs)
 |      Add a tile layer to the map. See TileLayer for options.
 |  
 |  render(self, **kwargs)
 |      Renders the HTML representation of the element.
 |  
 |  ----------------------------------------------------------------------
 |  Methods inherited from branca.element.Element:
 |  
 |  add_child(self, child, name=None, index=None)
 |      Add a child.
 |  
 |  add_children(self, child, name=None, index=None)
 |      Add a child.
 |  
 |  add_to(self, parent, name=None, index=None)
 |      Add element to a parent.
 |  
 |  get_bounds(self)
 |      Computes the bounds of the object and all it's children
 |      in the form [[lat_min, lon_min], [lat_max, lon_max]].
 |  
 |  get_name(self)
 |      Returns a string representation of the object.
 |      This string has to be unique and to be a python and
 |      javascript-compatible
 |      variable name.
 |  
 |  get_root(self)
 |      Returns the root of the elements tree.
 |  
 |  save(self, outfile, close_file=True, **kwargs)
 |      Saves an Element into a file.
 |      
 |      Parameters
 |      ----------
 |      outfile : str or file object
 |          The file (or filename) where you want to output the html.
 |      close_file : bool, default True
 |          Whether the file has to be closed after write.
 |  
 |  to_dict(self, depth=-1, ordered=True, **kwargs)
 |      Returns a dict representation of the object.
 |  
 |  to_json(self, depth=-1, **kwargs)
 |      Returns a JSON representation of the object.
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from branca.element.Element:
 |  
 |  __dict__
 |      dictionary for instance variables (if defined)
 |  
 |  __weakref__
 |      list of weak references to the object (if defined)


















In [101]:



    


m.to_dict()



    


















    
Out[101]:








OrderedDict([('name', 'Map'),
             ('id', 'e23239bf25fe4c978b207a9fe876c64f'),
             ('children',
              OrderedDict([('cartodbpositron',
                            OrderedDict([('name', 'TileLayer'),
                                         ('id',
                                          '57d1444e0dad4180b8aaf4af5e68ee08'),
                                         ('children', OrderedDict())])),
                           ('regiones_censales',
                            OrderedDict([('name', 'GeoJson'),
                                         ('id',
                                          '4f02efbd265d4dcd9db87fe6850c1ad8'),
                                         ('children', OrderedDict())]))]))])

















In [104]:



    


data_censo.iloc[[0], :]



    


















    
Out[104]:










  
    

      
      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
      92188.616635
      1352.569111
      POLYGON ((2.07086332857145 41.35740405853809, ...
    

  



1 rows × 22 columns



















In [183]:



    


m = folium.Map([41.37, 2.1], tiles='cartodbpositron', zoom_start=13)


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 create_function_style(features):
    style_function = lambda x: {'fillColor': matplotlib.colors.rgb2hex(colors[splitter(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)):
    style_function = create_function_style(pro_ind.iloc[i, :]['Pro_ind'])
    geo_cens = folium.folium.GeoJson(data_censo.iloc[[i], :],
                                     style_function=style_function,
                                     highlight_function=highlight_function,
                                     overlay=True,
                                     control=False)
    geo_cens.add_child(folium.features.Popup(data_censo.iloc[i, :]['CUSEC']))
    geo_cens.add_to(m, data_censo.iloc[i, :]['CUSEC'])
m



    


















    
Out[183]:
























In [178]:



    


matplotlib.colors.rgb2hex(colors[0])



    


















    
Out[178]:








u'#d22b27'

















In [161]:



    


sns.palplot(colors)



    


















    
























In [140]:



    


import seaborn as sns
colors = sns.color_palette("RdBu_r", 7)
colors[0]



    


















    
Out[140]:








(0.16339870177063293, 0.4449827098378949, 0.69750097919912901)

















In [ ]:



    


def create_abs_splits(values, extremes):
    extremes[0], extremes[-1]



    











In [141]:



    


np.linspace(0, 1, 11)



    


















    
Out[141]:








array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ])

















In [ ]:



    






    











In [ ]:



    






    











In [ ]:

Content source: tgquintela/temporal_tasks

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