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In [1]:

    
# -*- coding: UTF-8 -*-

# Render our plots inline
%matplotlib inline 

import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import re
import seaborn as sns



In [2]:

    
# Download and clean from OECD: 
# Demographic Statistics_TL3: Regional typology (urban=1/intermediate=2/rural=3,4,5)



In [3]:

    
# Load csv file first
regional = pd.read_csv("OECD-Regional-Well-Being-Data-File_last_score.csv", encoding="utf-8")



In [4]:

    
regional









    Out[4]:






  
    
      
      Country
      Region
      Region Code
      Education
      Jobs
      Income
      Safety
      Health
      Environment
      Civic engagement
      Accessiblity to services
      Housing
      Community
      Life satisfaction
    
  
  
    
      0
      Australia
      New South Wales
      AU1
      8
      8.1
      6.8
      8.8
      9.0
      9.8
      10.0
      7.2
      7.2
      8.9
      7.8
    
    
      1
      Australia
      Victoria
      AU2
      8.1
      7.9
      5.9
      9.5
      9.5
      8.6
      10.0
      7.5
      7.8
      9.3
      8.5
    
    
      2
      Australia
      Queensland
      AU3
      7.8
      8.1
      6.3
      9.5
      8.3
      9.9
      10.0
      7.7
      8.3
      8.6
      8.1
    
    
      3
      Australia
      South Australia
      AU4
      7.3
      7.8
      6.1
      9.0
      8.5
      9.4
      10.0
      7.2
      8.3
      8.6
      8.5
    
    
      4
      Australia
      Western Australia
      AU5
      7.6
      8.8
      7.9
      8.6
      9.3
      9.6
      10.0
      7.8
      8.9
      8.5
      7.8
    
    
      5
      Australia
      Tasmania
      AU6
      6.5
      7.6
      5.4
      8.8
      5.4
      10.0
      10.0
      6.8
      8.3
      8.6
      9.6
    
    
      6
      Australia
      Northern Territory
      AU7
      8.1
      8.7
      8.2
      0.0
      2.4
      9.2
      8.4
      7.8
      5.6
      10
      7
    
    
      7
      Australia
      Australian Capital Territory
      AU8
      9.5
      9.3
      10.0
      10.0
      9.3
      9.1
      10.0
      8.7
      8.3
      9.8
      9.6
    
    
      8
      Austria
      Burgenland
      AT11
      8.8
      7.8
      5.7
      9.7
      6.7
      3.5
      8.6
      8.0
      6.1
      8.3
      7.8
    
    
      9
      Austria
      Lower Austria
      AT12
      8.5
      8.2
      5.9
      9.8
      6.6
      2.6
      8.1
      7.4
      5.6
      7.8
      8.1
    
    
      10
      Austria
      Vienna
      AT13
      8.8
      5.8
      5.5
      9.0
      5.7
      2.5
      5.5
      8.4
      4.4
      7.6
      7.8
    
    
      11
      Austria
      Carinthia
      AT21
      9.1
      7.5
      5.4
      10.0
      7.6
      0.3
      6.2
      7.0
      5.6
      8.5
      8.1
    
    
      12
      Austria
      Styria
      AT22
      8.8
      8.0
      5.5
      10.0
      7.7
      4.3
      6.7
      7.7
      5.6
      8.7
      8.5
    
    
      13
      Austria
      Upper Austria
      AT31
      8.1
      8.8
      5.7
      10.0
      7.2
      3.7
      7.4
      7.8
      5.6
      8.9
      8.5
    
    
      14
      Austria
      Salzburg
      AT32
      8.7
      8.7
      5.8
      9.8
      8.1
      3.5
      6.7
      8.6
      5
      8.6
      8.1
    
    
      15
      Austria
      Tyrol
      AT33
      8.2
      8.9
      5.5
      9.1
      8.5
      5.8
      4.8
      7.8
      4.4
      9.7
      8.9
    
    
      16
      Austria
      Vorarlberg
      AT34
      8.1
      8.9
      5.9
      10.0
      8.3
      3.2
      4.5
      8.1
      5.6
      8.2
      7.4
    
    
      17
      Belgium
      Brussels-Capital Region
      BE1
      6.8
      2.2
      4.2
      6.0
      6.3
      2.4
      10.0
      7.7
      6.7
      7.4
      6.3
    
    
      18
      Belgium
      Flemish Region (Vlaams Gewest)
      BE2
      8.1
      7.3
      5.0
      8.3
      7.7
      2.0
      10.0
      8.7
      7.8
      9.1
      7.8
    
    
      19
      Belgium
      Wallonia (Région wallonne)
      BE3
      7.4
      4.3
      4.2
      7.4
      4.5
      3.0
      9.7
      7.5
      7.8
      7.4
      6.3
    
    
      20
      Canada
      Newfoundland and Labrador
      CA10
      8.8
      4.7
      5.5
      9.8
      5.1
      9.3
      3.5
      8.0
      10
      8
      10
    
    
      21
      Canada
      Prince Edward Island
      CA11
      8.5
      6.3
      4.5
      10.0
      6.3
      9.5
      7.3
      8.1
      9.4
      4.3
      10
    
    
      22
      Canada
      Nova Scotia
      CA12
      9
      5.9
      4.7
      7.8
      6.1
      9.6
      5.8
      8.2
      10
      8.1
      8.5
    
    
      23
      Canada
      New Brunswick
      CA13
      8.7
      5.7
      4.7
      9.5
      6.8
      9.2
      6.6
      7.7
      10
      8.6
      8.1
    
    
      24
      Canada
      Quebec
      CA24
      9
      6.6
      4.6
      8.6
      8.4
      7.9
      4.6
      7.9
      8.3
      8.8
      8.5
    
    
      25
      Canada
      Ontario
      CA35
      9.4
      6.8
      5.3
      8.8
      8.4
      6.5
      5.1
      8.6
      8.3
      8.1
      8.1
    
    
      26
      Canada
      Manitoba
      CA46
      8.7
      7.5
      4.8
      3.6
      5.9
      6.9
      5.2
      8.2
      7.8
      9.5
      9.3
    
    
      27
      Canada
      Saskatchewan
      CA47
      8.9
      8.3
      5.8
      6.2
      5.5
      6.5
      6.0
      8.1
      9.4
      7.1
      8.5
    
    
      28
      Canada
      Alberta
      CA48
      9.1
      8.6
      7.3
      7.1
      7.8
      7.4
      5.3
      9.1
      8.9
      7.9
      8.5
    
    
      29
      Canada
      British Columbia
      CA59
      9.6
      6.9
      5.6
      8.1
      9.1
      8.1
      5.6
      9.4
      8.3
      9.8
      9.3
    
    
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    
    
      365
      United States
      Massachusetts
      US25
      9.7
      8.0
      10.0
      7.4
      6.7
      7.7
      5.7
      8.7
      8.9
      9.3
      7
    
    
      366
      United States
      Michigan
      US26
      9.7
      6.9
      8.1
      0.0
      3.9
      6.8
      4.7
      7.4
      10
      10
      6.7
    
    
      367
      United States
      Minnesota
      US27
      9.8
      9.3
      9.9
      7.2
      7.1
      5.8
      6.3
      8.3
      10
      8.7
      8.5
    
    
      368
      United States
      Mississippi
      US28
      9
      5.4
      7.2
      0.0
      0.0
      7.8
      6.6
      5.4
      8.9
      9.6
      8.1
    
    
      369
      United States
      Missouri
      US29
      9.6
      7.7
      8.6
      0.3
      3.2
      6.7
      4.0
      7.3
      10
      5.8
      7.8
    
    
      370
      United States
      Montana
      US30
      9.7
      8.6
      8.3
      7.1
      3.1
      8.6
      4.5
      7.6
      10
      9.8
      5.6
    
    
      371
      United States
      Nebraska
      US31
      9.5
      9.8
      10.0
      5.7
      5.7
      6.4
      3.5
      7.8
      10
      6
      10
    
    
      372
      United States
      Nevada
      US32
      8.7
      6.8
      8.3
      0.9
      4.1
      8.3
      2.6
      7.9
      7.8
      8.1
      6.3
    
    
      373
      United States
      New Hampshire
      US33
      9.9
      9.2
      10.0
      7.9
      5.1
      8.0
      5.4
      8.8
      10
      10
      6.7
    
    
      374
      United States
      New Jersey
      US34
      9.5
      7.5
      10.0
      3.1
      6.4
      5.5
      3.6
      8.6
      8.3
      6.8
      7.4
    
    
      375
      United States
      New Mexico
      US35
      8.9
      6.3
      7.7
      0.5
      4.9
      8.4
      3.5
      6.2
      8.3
      8.2
      7.4
    
    
      376
      United States
      New York
      US36
      9.2
      7.0
      10.0
      5.2
      6.9
      5.4
      2.8
      8.1
      7.2
      8.4
      7.4
    
    
      377
      United States
      North Carolina
      US37
      9.2
      6.9
      8.1
      2.6
      3.7
      7.3
      5.2
      7.3
      9.4
      7.7
      8.5
    
    
      378
      United States
      North Dakota
      US38
      9.9
      10.0
      10.0
      7.1
      4.3
      7.2
      4.0
      7.9
      10
      6.4
      10
    
    
      379
      United States
      Ohio
      US39
      9.6
      7.7
      8.6
      4.1
      3.3
      5.7
      3.9
      7.6
      10
      9.8
      6.3
    
    
      380
      United States
      Oklahoma
      US40
      9.1
      7.5
      8.9
      2.1
      1.1
      7.3
      1.3
      6.7
      8.9
      7.8
      7
    
    
      381
      United States
      Oregon
      US41
      9.3
      7.1
      8.3
      7.4
      5.4
      9.2
      4.9
      8.3
      8.3
      7.4
      7
    
    
      382
      United States
      Pennsylvania
      US42
      9.7
      7.8
      9.8
      2.9
      4.4
      5.5
      3.5
      7.8
      10
      8.7
      7
    
    
      383
      United States
      Rhode Island
      US44
      9.3
      7.3
      10.0
      5.9
      4.5
      8.0
      3.7
      8.4
      8.9
      9.9
      8.9
    
    
      384
      United States
      South Carolina
      US45
      9.2
      6.7
      7.6
      0.2
      2.6
      8.0
      4.2
      6.8
      10
      5.2
      9.6
    
    
      385
      United States
      South Dakota
      US46
      9.5
      9.5
      10.0
      6.7
      5.4
      7.0
      3.4
      7.4
      10
      9.9
      7.4
    
    
      386
      United States
      Tennessee
      US47
      9.4
      6.7
      8.6
      2.2
      1.7
      6.9
      2.1
      6.8
      9.4
      8.8
      8.5
    
    
      387
      United States
      Texas
      US48
      8.5
      7.6
      9.4
      3.4
      4.5
      7.7
      1.6
      7.2
      7.2
      6.9
      8.1
    
    
      388
      United States
      Utah
      US49
      9.5
      8.4
      7.7
      7.9
      6.0
      9.1
      2.4
      8.5
      7.2
      9
      7.4
    
    
      389
      United States
      Vermont
      US50
      9.7
      9.3
      9.8
      8.1
      4.9
      7.4
      3.9
      8.1
      10
      9.4
      10
    
    
      390
      United States
      Virginia
      US51
      9.6
      8.0
      10.0
      4.3
      5.1
      5.9
      4.8
      8.1
      9.4
      7.7
      8.5
    
    
      391
      United States
      Washington
      US53
      9.5
      7.3
      10.0
      6.9
      6.1
      8.8
      4.4
      8.4
      8.3
      8.9
      7.8
    
    
      392
      United States
      West Virginia
      US54
      9.5
      6.2
      7.5
      5.2
      0.7
      6.5
      0.2
      6.8
      10
      8
      6.3
    
    
      393
      United States
      Wisconsin
      US55
      9.7
      8.6
      9.1
      6.0
      5.7
      6.1
      6.3
      7.8
      10
      8.3
      7.8
    
    
      394
      United States
      Wyoming
      US56
      9.8
      8.8
      10.0
      5.9
      3.4
      8.1
      2.9
      8.0
      10
      10
      10
    
  

395 rows × 14 columns



In [5]:

    
#regional.replace(np.nan, 0)
regional = regional.fillna(0)



In [6]:

    
regional.dtypes









    Out[6]:





Country                      object
Region                       object
Region Code                  object
Education                    object
Jobs                        float64
Income                      float64
Safety                      float64
Health                      float64
Environment                 float64
Civic engagement            float64
Accessiblity to services    float64
Housing                      object
Community                    object
Life satisfaction            object
dtype: object



In [7]:

    
columns = list(regional.columns.values)
for r, i in enumerate(regional.iterrows()):
    for k,j in enumerate(i[1]):
        if k > 2:
            try:
                val = np.float64(j)
                regional.set_value(r, columns[k], val)
            except Exception as e:
                print "no", e, k, r, columns[k], regional.iloc[r][columns[k]]
                print j
                print regional.iloc[r][columns[k]]
                regional.set_value(r, columns[k], 0)
                print regional.iloc[r][columns[k]]









    



no could not convert string to float: .. 3 30 Education ..
..
..
0
no could not convert string to float: .. 12 30 Community ..
..
..
0
no could not convert string to float: .. 13 30 Life satisfaction ..
..
..
0
no could not convert string to float: .. 3 31 Education ..
..
..
0
no could not convert string to float: .. 12 31 Community ..
..
..
0
no could not convert string to float: .. 13 31 Life satisfaction ..
..
..
0
no could not convert string to float: .. 3 32 Education ..
..
..
0
no could not convert string to float: .. 12 32 Community ..
..
..
0
no could not convert string to float: .. 13 32 Life satisfaction ..
..
..
0
no could not convert string to float: .. 11 46 Housing ..
..
..
0
no could not convert string to float: .. 11 47 Housing ..
..
..
0
no could not convert string to float: .. 12 70 Community ..
..
..
0
no could not convert string to float: .. 13 70 Life satisfaction ..
..
..
0



In [8]:

    
for r, i in enumerate(regional.iterrows()):
    for k,j in enumerate(i[1]):
        if k > 2:
            print regional.iloc[r][columns[k]], type(regional.iloc[r][columns[k]])









    



8.0 <type 'numpy.float64'>
8.1 <type 'numpy.float64'>
6.8 <type 'numpy.float64'>
8.8 <type 'numpy.float64'>
9.0 <type 'numpy.float64'>
9.8 <type 'numpy.float64'>
10.0 <type 'numpy.float64'>
7.2 <type 'numpy.float64'>
7.2 <type 'numpy.float64'>
8.9 <type 'numpy.float64'>
7.8 <type 'numpy.float64'>
8.1 <type 'numpy.float64'>
7.9 <type 'numpy.float64'>
5.9 <type 'numpy.float64'>
9.5 <type 'numpy.float64'>
9.5 <type 'numpy.float64'>
8.6 <type 'numpy.float64'>
10.0 <type 'numpy.float64'>
7.5 <type 'numpy.float64'>
7.8 <type 'numpy.float64'>
9.3 <type 'numpy.float64'>
8.5 <type 'numpy.float64'>
7.8 <type 'numpy.float64'>
8.1 <type 'numpy.float64'>
6.3 <type 'numpy.float64'>
9.5 <type 'numpy.float64'>
8.3 <type 'numpy.float64'>
9.9 <type 'numpy.float64'>
10.0 <type 'numpy.float64'>
7.7 <type 'numpy.float64'>
8.3 <type 'numpy.float64'>
8.6 <type 'numpy.float64'>
8.1 <type 'numpy.float64'>
7.3 <type 'numpy.float64'>
7.8 <type 'numpy.float64'>
6.1 <type 'numpy.float64'>
9.0 <type 'numpy.float64'>
8.5 <type 'numpy.float64'>
9.4 <type 'numpy.float64'>
10.0 <type 'numpy.float64'>
7.2 <type 'numpy.float64'>
8.3 <type 'numpy.float64'>
8.6 <type 'numpy.float64'>
8.5 <type 'numpy.float64'>
7.6 <type 'numpy.float64'>
8.8 <type 'numpy.float64'>
7.9 <type 'numpy.float64'>
8.6 <type 'numpy.float64'>
9.3 <type 'numpy.float64'>
9.6 <type 'numpy.float64'>
10.0 <type 'numpy.float64'>
7.8 <type 'numpy.float64'>
8.9 <type 'numpy.float64'>
8.5 <type 'numpy.float64'>
7.8 <type 'numpy.float64'>
6.5 <type 'numpy.float64'>
7.6 <type 'numpy.float64'>
5.4 <type 'numpy.float64'>
8.8 <type 'numpy.float64'>
5.4 <type 'numpy.float64'>
10.0 <type 'numpy.float64'>
10.0 <type 'numpy.float64'>
6.8 <type 'numpy.float64'>
8.3 <type 'numpy.float64'>
8.6 <type 'numpy.float64'>
9.6 <type 'numpy.float64'>
8.1 <type 'numpy.float64'>
8.7 <type 'numpy.float64'>
8.2 <type 'numpy.float64'>
0.0 <type 'numpy.float64'>
2.4 <type 'numpy.float64'>
9.2 <type 'numpy.float64'>
8.4 <type 'numpy.float64'>
7.8 <type 'numpy.float64'>
5.6 <type 'numpy.float64'>
10.0 <type 'numpy.float64'>
7.0 <type 'numpy.float64'>
9.5 <type 'numpy.float64'>
9.3 <type 'numpy.float64'>
10.0 <type 'numpy.float64'>
10.0 <type 'numpy.float64'>
9.3 <type 'numpy.float64'>
9.1 <type 'numpy.float64'>
10.0 <type 'numpy.float64'>
8.7 <type 'numpy.float64'>
8.3 <type 'numpy.float64'>
9.8 <type 'numpy.float64'>
9.6 <type 'numpy.float64'>
8.8 <type 'numpy.float64'>
7.8 <type 'numpy.float64'>
5.7 <type 'numpy.float64'>
9.7 <type 'numpy.float64'>
6.7 <type 'numpy.float64'>
3.5 <type 'numpy.float64'>
8.6 <type 'numpy.float64'>
8.0 <type 'numpy.float64'>
6.1 <type 'numpy.float64'>
8.3 <type 'numpy.float64'>
7.8 <type 'numpy.float64'>
8.5 <type 'numpy.float64'>
8.2 <type 'numpy.float64'>
5.9 <type 'numpy.float64'>
9.8 <type 'numpy.float64'>
6.6 <type 'numpy.float64'>
2.6 <type 'numpy.float64'>
8.1 <type 'numpy.float64'>
7.4 <type 'numpy.float64'>
5.6 <type 'numpy.float64'>
7.8 <type 'numpy.float64'>
8.1 <type 'numpy.float64'>
8.8 <type 'numpy.float64'>
5.8 <type 'numpy.float64'>
5.5 <type 'numpy.float64'>
9.0 <type 'numpy.float64'>
5.7 <type 'numpy.float64'>
2.5 <type 'numpy.float64'>
5.5 <type 'numpy.float64'>
8.4 <type 'numpy.float64'>
4.4 <type 'numpy.float64'>
7.6 <type 'numpy.float64'>
7.8 <type 'numpy.float64'>
9.1 <type 'numpy.float64'>
7.5 <type 'numpy.float64'>
5.4 <type 'numpy.float64'>
10.0 <type 'numpy.float64'>
7.6 <type 'numpy.float64'>
0.3 <type 'numpy.float64'>
6.2 <type 'numpy.float64'>
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8.7 <type 'numpy.float64'>
7.0 <type 'numpy.float64'>
9.3 <type 'numpy.float64'>
7.3 <type 'numpy.float64'>
10.0 <type 'numpy.float64'>
5.9 <type 'numpy.float64'>
4.5 <type 'numpy.float64'>
8.0 <type 'numpy.float64'>
3.7 <type 'numpy.float64'>
8.4 <type 'numpy.float64'>
8.9 <type 'numpy.float64'>
9.9 <type 'numpy.float64'>
8.9 <type 'numpy.float64'>
9.2 <type 'numpy.float64'>
6.7 <type 'numpy.float64'>
7.6 <type 'numpy.float64'>
0.2 <type 'numpy.float64'>
2.6 <type 'numpy.float64'>
8.0 <type 'numpy.float64'>
4.2 <type 'numpy.float64'>
6.8 <type 'numpy.float64'>
10.0 <type 'numpy.float64'>
5.2 <type 'numpy.float64'>
9.6 <type 'numpy.float64'>
9.5 <type 'numpy.float64'>
9.5 <type 'numpy.float64'>
10.0 <type 'numpy.float64'>
6.7 <type 'numpy.float64'>
5.4 <type 'numpy.float64'>
7.0 <type 'numpy.float64'>
3.4 <type 'numpy.float64'>
7.4 <type 'numpy.float64'>
10.0 <type 'numpy.float64'>
9.9 <type 'numpy.float64'>
7.4 <type 'numpy.float64'>
9.4 <type 'numpy.float64'>
6.7 <type 'numpy.float64'>
8.6 <type 'numpy.float64'>
2.2 <type 'numpy.float64'>
1.7 <type 'numpy.float64'>
6.9 <type 'numpy.float64'>
2.1 <type 'numpy.float64'>
6.8 <type 'numpy.float64'>
9.4 <type 'numpy.float64'>
8.8 <type 'numpy.float64'>
8.5 <type 'numpy.float64'>
8.5 <type 'numpy.float64'>
7.6 <type 'numpy.float64'>
9.4 <type 'numpy.float64'>
3.4 <type 'numpy.float64'>
4.5 <type 'numpy.float64'>
7.7 <type 'numpy.float64'>
1.6 <type 'numpy.float64'>
7.2 <type 'numpy.float64'>
7.2 <type 'numpy.float64'>
6.9 <type 'numpy.float64'>
8.1 <type 'numpy.float64'>
9.5 <type 'numpy.float64'>
8.4 <type 'numpy.float64'>
7.7 <type 'numpy.float64'>
7.9 <type 'numpy.float64'>
6.0 <type 'numpy.float64'>
9.1 <type 'numpy.float64'>
2.4 <type 'numpy.float64'>
8.5 <type 'numpy.float64'>
7.2 <type 'numpy.float64'>
9.0 <type 'numpy.float64'>
7.4 <type 'numpy.float64'>
9.7 <type 'numpy.float64'>
9.3 <type 'numpy.float64'>
9.8 <type 'numpy.float64'>
8.1 <type 'numpy.float64'>
4.9 <type 'numpy.float64'>
7.4 <type 'numpy.float64'>
3.9 <type 'numpy.float64'>
8.1 <type 'numpy.float64'>
10.0 <type 'numpy.float64'>
9.4 <type 'numpy.float64'>
10.0 <type 'numpy.float64'>
9.6 <type 'numpy.float64'>
8.0 <type 'numpy.float64'>
10.0 <type 'numpy.float64'>
4.3 <type 'numpy.float64'>
5.1 <type 'numpy.float64'>
5.9 <type 'numpy.float64'>
4.8 <type 'numpy.float64'>
8.1 <type 'numpy.float64'>
9.4 <type 'numpy.float64'>
7.7 <type 'numpy.float64'>
8.5 <type 'numpy.float64'>
9.5 <type 'numpy.float64'>
7.3 <type 'numpy.float64'>
10.0 <type 'numpy.float64'>
6.9 <type 'numpy.float64'>
6.1 <type 'numpy.float64'>
8.8 <type 'numpy.float64'>
4.4 <type 'numpy.float64'>
8.4 <type 'numpy.float64'>
8.3 <type 'numpy.float64'>
8.9 <type 'numpy.float64'>
7.8 <type 'numpy.float64'>
9.5 <type 'numpy.float64'>
6.2 <type 'numpy.float64'>
7.5 <type 'numpy.float64'>
5.2 <type 'numpy.float64'>
0.7 <type 'numpy.float64'>
6.5 <type 'numpy.float64'>
0.2 <type 'numpy.float64'>
6.8 <type 'numpy.float64'>
10.0 <type 'numpy.float64'>
8.0 <type 'numpy.float64'>
6.3 <type 'numpy.float64'>
9.7 <type 'numpy.float64'>
8.6 <type 'numpy.float64'>
9.1 <type 'numpy.float64'>
6.0 <type 'numpy.float64'>
5.7 <type 'numpy.float64'>
6.1 <type 'numpy.float64'>
6.3 <type 'numpy.float64'>
7.8 <type 'numpy.float64'>
10.0 <type 'numpy.float64'>
8.3 <type 'numpy.float64'>
7.8 <type 'numpy.float64'>
9.8 <type 'numpy.float64'>
8.8 <type 'numpy.float64'>
10.0 <type 'numpy.float64'>
5.9 <type 'numpy.float64'>
3.4 <type 'numpy.float64'>
8.1 <type 'numpy.float64'>
2.9 <type 'numpy.float64'>
8.0 <type 'numpy.float64'>
10.0 <type 'numpy.float64'>
10.0 <type 'numpy.float64'>
10.0 <type 'numpy.float64'>



In [17]:

    
print regional.iloc[31]["Education"]
print type(regional.iloc[31]["Education"])









    



0
<type 'int'>



In [23]:

    
# convert everything to numbers
test = regional.apply(lambda x: pd.to_numeric(x, errors='ignore'))



In [25]:

    
test.dtypes









    Out[25]:





Country                      object
Region                       object
Region Code                  object
Education                   float64
Jobs                        float64
Income                      float64
Safety                      float64
Health                      float64
Environment                 float64
Civic engagement            float64
Accessiblity to services    float64
Housing                     float64
Community                   float64
Life satisfaction           float64
dtype: object



In [26]:

    
regional = test.fillna(0)



In [29]:

    
regional.dtypes









    Out[29]:





Country                      object
Region                       object
Region Code                  object
Education                   float64
Jobs                        float64
Income                      float64
Safety                      float64
Health                      float64
Environment                 float64
Civic engagement            float64
Accessiblity to services    float64
Housing                     float64
Community                   float64
Life satisfaction           float64
dtype: object



In [30]:

    
national = regional.groupby(['Country'], as_index=False)



In [31]:

    
national.mean()









    Out[31]:






  
    
      
      Country
      Education
      Jobs
      Income
      Safety
      Health
      Environment
      Civic engagement
      Accessiblity to services
      Housing
      Community
      Life satisfaction
    
  
  
    
      0
      Australia
      7.862500
      8.287500
      7.075000
      8.025000
      7.712500
      9.450000
      9.800000
      7.587500
      7.837500
      9.037500
      8.362500
    
    
      1
      Austria
      8.566667
      8.066667
      5.655556
      9.711111
      7.377778
      3.266667
      6.500000
      7.866667
      5.322222
      8.477778
      8.133333
    
    
      2
      Belgium
      7.433333
      4.600000
      4.466667
      7.233333
      6.166667
      2.466667
      9.900000
      7.966667
      7.433333
      7.966667
      6.800000
    
    
      3
      Canada
      6.900000
      6.600000
      5.623077
      6.884615
      5.884615
      8.461538
      5.361538
      8.100000
      8.146154
      6.169231
      6.830769
    
    
      4
      Chile
      7.246667
      6.786667
      0.900000
      5.480000
      3.573333
      7.900000
      0.446667
      1.153333
      1.533333
      4.686667
      5.706667
    
    
      5
      Czech Republic
      9.912500
      7.525000
      2.500000
      7.887500
      3.037500
      1.900000
      2.962500
      7.337500
      2.775000
      6.562500
      5.012500
    
    
      6
      Denmark
      7.000000
      8.080000
      3.960000
      9.680000
      5.880000
      6.080000
      9.220000
      8.640000
      5.240000
      9.700000
      9.260000
    
    
      7
      Estonia TL3
      9.240000
      7.000000
      1.420000
      4.760000
      1.640000
      6.720000
      3.500000
      7.400000
      2.160000
      6.100000
      1.260000
    
    
      8
      Finland
      8.540000
      7.400000
      4.360000
      9.720000
      7.320000
      7.980000
      4.580000
      8.520000
      5.800000
      7.080000
      7.120000
    
    
      9
      France
      7.818182
      5.531818
      4.895455
      8.754545
      8.431818
      5.286364
      8.227273
      7.318182
      5.268182
      8.077273
      5.818182
    
    
      10
      Germany
      8.956250
      8.150000
      5.387500
      9.087500
      6.175000
      3.962500
      5.593750
      8.818750
      7.331250
      8.862500
      5.706250
    
    
      11
      Greece
      5.484615
      0.653846
      1.723077
      5.100000
      7.900000
      3.769231
      4.030769
      5.492308
      3.346154
      3.769231
      1.407692
    
    
      12
      Hungary
      8.728571
      5.714286
      1.785714
      8.314286
      0.442857
      2.500000
      3.342857
      6.928571
      1.114286
      6.371429
      0.157143
    
    
      13
      Iceland
      5.900000
      9.550000
      3.700000
      10.000000
      8.000000
      9.750000
      8.150000
      9.750000
      3.900000
      9.750000
      7.750000
    
    
      14
      Ireland
      7.550000
      5.200000
      4.450000
      7.550000
      6.850000
      8.400000
      5.500000
      7.700000
      6.650000
      9.850000
      7.400000
    
    
      15
      Israel
      8.966667
      7.400000
      1.716667
      8.333333
      8.216667
      0.583333
      3.833333
      6.283333
      1.400000
      6.700000
      8.183333
    
    
      16
      Italy
      5.447619
      4.333333
      4.347619
      9.033333
      9.133333
      3.938095
      6.814286
      6.600000
      4.723810
      6.704762
      3.947619
    
    
      17
      Japan
      9.030000
      9.710000
      4.380000
      9.570000
      9.720000
      4.360000
      1.360000
      6.260000
      6.510000
      7.150000
      3.140000
    
    
      18
      Korea
      7.528571
      8.385714
      3.457143
      7.328571
      8.428571
      0.842857
      6.885714
      10.000000
      2.857143
      1.942857
      2.900000
    
    
      19
      Luxembourg
      8.100000
      6.700000
      7.100000
      9.500000
      8.100000
      5.100000
      10.000000
      9.900000
      6.100000
      6.200000
      6.700000
    
    
      20
      Mexico
      1.168750
      6.915625
      0.165625
      0.362500
      1.450000
      5.684375
      3.737500
      1.406250
      0.453125
      3.315625
      7.450000
    
    
      21
      Netherlands
      6.950000
      7.741667
      3.858333
      9.533333
      7.175000
      3.983333
      6.691667
      9.916667
      6.350000
      8.883333
      8.866667
    
    
      22
      New Zealand
      6.257143
      8.857143
      3.435714
      9.314286
      5.750000
      9.935714
      6.578571
      6.778571
      7.507143
      9.242857
      8.300000
    
    
      23
      Norway
      8.071429
      9.285714
      5.257143
      9.428571
      7.471429
      8.857143
      7.328571
      8.857143
      6.200000
      8.714286
      9.114286
    
    
      24
      Poland
      9.731250
      5.137500
      2.256250
      7.850000
      2.243750
      3.062500
      0.693750
      6.556250
      0.537500
      7.525000
      2.850000
    
    
      25
      Portugal
      1.857143
      4.857143
      3.557143
      8.285714
      5.500000
      7.657143
      1.714286
      5.557143
      5.500000
      5.357143
      0.685714
    
    
      26
      Slovak Republic
      9.850000
      5.050000
      2.950000
      8.250000
      1.325000
      2.350000
      0.300000
      7.475000
      1.100000
      7.025000
      3.875000
    
    
      27
      Slovenia
      9.000000
      6.100000
      3.050000
      7.500000
      6.450000
      3.850000
      1.300000
      7.350000
      2.800000
      8.050000
      3.550000
    
    
      28
      Spain
      4.515789
      1.878947
      3.826316
      9.542105
      9.073684
      6.263158
      4.942105
      6.921053
      5.478947
      8.752632
      5.247368
    
    
      29
      Sweden
      8.375000
      8.150000
      4.462500
      9.462500
      7.600000
      7.925000
      9.250000
      8.750000
      4.712500
      8.125000
      8.550000
    
    
      30
      Switzerland
      8.528571
      9.300000
      5.114286
      9.642857
      9.257143
      3.314286
      0.600000
      8.742857
      4.257143
      8.971429
      9.085714
    
    
      31
      Turkey
      0.807692
      4.100000
      0.619231
      6.676923
      2.961538
      2.653846
      9.415385
      2.396154
      0.642308
      2.188462
      1.253846
    
    
      32
      United Kingdom
      7.866667
      7.775000
      4.875000
      9.133333
      6.725000
      6.325000
      4.400000
      9.033333
      6.408333
      9.083333
      6.575000
    
    
      33
      United States
      9.421569
      7.662745
      9.025490
      4.037255
      4.350980
      7.176471
      3.811765
      7.650980
      8.988235
      8.280392
      7.743137



In [32]:

    
national.mean().to_csv("national.csv", encoding="utf-8")



In [33]:

    
ax = national.mean().plot(kind="bar", figsize=[16,6])
ax.set_xticklabels(list(national.mean()['Country']))









    Out[33]:





[<matplotlib.text.Text at 0x10874b310>,
 <matplotlib.text.Text at 0x107f58c10>,
 <matplotlib.text.Text at 0x10906e890>,
 <matplotlib.text.Text at 0x109077050>,
 <matplotlib.text.Text at 0x1090777d0>,
 <matplotlib.text.Text at 0x109077f50>,
 <matplotlib.text.Text at 0x109080710>,
 <matplotlib.text.Text at 0x109080e90>,
 <matplotlib.text.Text at 0x10908b650>,
 <matplotlib.text.Text at 0x10909a350>,
 <matplotlib.text.Text at 0x10909a950>,
 <matplotlib.text.Text at 0x1090a4110>,
 <matplotlib.text.Text at 0x1090a4890>,
 <matplotlib.text.Text at 0x1090ac050>,
 <matplotlib.text.Text at 0x1090ac7d0>,
 <matplotlib.text.Text at 0x1090acb10>,
 <matplotlib.text.Text at 0x1090b7390>,
 <matplotlib.text.Text at 0x1090b7b10>,
 <matplotlib.text.Text at 0x1090c12d0>,
 <matplotlib.text.Text at 0x1090c1a50>,
 <matplotlib.text.Text at 0x1090ca210>,
 <matplotlib.text.Text at 0x1090ca990>,
 <matplotlib.text.Text at 0x1090d4150>,
 <matplotlib.text.Text at 0x1090d48d0>,
 <matplotlib.text.Text at 0x1090df090>,
 <matplotlib.text.Text at 0x1090df810>,
 <matplotlib.text.Text at 0x1090dff90>,
 <matplotlib.text.Text at 0x109093850>,
 <matplotlib.text.Text at 0x109077490>,
 <matplotlib.text.Text at 0x1090e7850>,
 <matplotlib.text.Text at 0x1090e7fd0>,
 <matplotlib.text.Text at 0x1090f2790>,
 <matplotlib.text.Text at 0x1090f2f10>,
 <matplotlib.text.Text at 0x1090fd6d0>]



In [34]:

    
regional.to_csv("regional.csv", encoding="utf-8")



In [ ]:

	Country	Region	Region Code	Education	Jobs	Income	Safety	Health	Environment	Civic engagement	Accessiblity to services	Housing	Community	Life satisfaction
0	Australia	New South Wales	AU1	8	8.1	6.8	8.8	9.0	9.8	10.0	7.2	7.2	8.9	7.8
1	Australia	Victoria	AU2	8.1	7.9	5.9	9.5	9.5	8.6	10.0	7.5	7.8	9.3	8.5
2	Australia	Queensland	AU3	7.8	8.1	6.3	9.5	8.3	9.9	10.0	7.7	8.3	8.6	8.1
3	Australia	South Australia	AU4	7.3	7.8	6.1	9.0	8.5	9.4	10.0	7.2	8.3	8.6	8.5
4	Australia	Western Australia	AU5	7.6	8.8	7.9	8.6	9.3	9.6	10.0	7.8	8.9	8.5	7.8
5	Australia	Tasmania	AU6	6.5	7.6	5.4	8.8	5.4	10.0	10.0	6.8	8.3	8.6	9.6
6	Australia	Northern Territory	AU7	8.1	8.7	8.2	0.0	2.4	9.2	8.4	7.8	5.6	10	7
7	Australia	Australian Capital Territory	AU8	9.5	9.3	10.0	10.0	9.3	9.1	10.0	8.7	8.3	9.8	9.6
8	Austria	Burgenland	AT11	8.8	7.8	5.7	9.7	6.7	3.5	8.6	8.0	6.1	8.3	7.8
9	Austria	Lower Austria	AT12	8.5	8.2	5.9	9.8	6.6	2.6	8.1	7.4	5.6	7.8	8.1
10	Austria	Vienna	AT13	8.8	5.8	5.5	9.0	5.7	2.5	5.5	8.4	4.4	7.6	7.8
11	Austria	Carinthia	AT21	9.1	7.5	5.4	10.0	7.6	0.3	6.2	7.0	5.6	8.5	8.1
12	Austria	Styria	AT22	8.8	8.0	5.5	10.0	7.7	4.3	6.7	7.7	5.6	8.7	8.5
13	Austria	Upper Austria	AT31	8.1	8.8	5.7	10.0	7.2	3.7	7.4	7.8	5.6	8.9	8.5
14	Austria	Salzburg	AT32	8.7	8.7	5.8	9.8	8.1	3.5	6.7	8.6	5	8.6	8.1
15	Austria	Tyrol	AT33	8.2	8.9	5.5	9.1	8.5	5.8	4.8	7.8	4.4	9.7	8.9
16	Austria	Vorarlberg	AT34	8.1	8.9	5.9	10.0	8.3	3.2	4.5	8.1	5.6	8.2	7.4
17	Belgium	Brussels-Capital Region	BE1	6.8	2.2	4.2	6.0	6.3	2.4	10.0	7.7	6.7	7.4	6.3
18	Belgium	Flemish Region (Vlaams Gewest)	BE2	8.1	7.3	5.0	8.3	7.7	2.0	10.0	8.7	7.8	9.1	7.8
19	Belgium	Wallonia (Région wallonne)	BE3	7.4	4.3	4.2	7.4	4.5	3.0	9.7	7.5	7.8	7.4	6.3
20	Canada	Newfoundland and Labrador	CA10	8.8	4.7	5.5	9.8	5.1	9.3	3.5	8.0	10	8	10
21	Canada	Prince Edward Island	CA11	8.5	6.3	4.5	10.0	6.3	9.5	7.3	8.1	9.4	4.3	10
22	Canada	Nova Scotia	CA12	9	5.9	4.7	7.8	6.1	9.6	5.8	8.2	10	8.1	8.5
23	Canada	New Brunswick	CA13	8.7	5.7	4.7	9.5	6.8	9.2	6.6	7.7	10	8.6	8.1
24	Canada	Quebec	CA24	9	6.6	4.6	8.6	8.4	7.9	4.6	7.9	8.3	8.8	8.5
25	Canada	Ontario	CA35	9.4	6.8	5.3	8.8	8.4	6.5	5.1	8.6	8.3	8.1	8.1
26	Canada	Manitoba	CA46	8.7	7.5	4.8	3.6	5.9	6.9	5.2	8.2	7.8	9.5	9.3
27	Canada	Saskatchewan	CA47	8.9	8.3	5.8	6.2	5.5	6.5	6.0	8.1	9.4	7.1	8.5
28	Canada	Alberta	CA48	9.1	8.6	7.3	7.1	7.8	7.4	5.3	9.1	8.9	7.9	8.5
29	Canada	British Columbia	CA59	9.6	6.9	5.6	8.1	9.1	8.1	5.6	9.4	8.3	9.8	9.3
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
365	United States	Massachusetts	US25	9.7	8.0	10.0	7.4	6.7	7.7	5.7	8.7	8.9	9.3	7
366	United States	Michigan	US26	9.7	6.9	8.1	0.0	3.9	6.8	4.7	7.4	10	10	6.7
367	United States	Minnesota	US27	9.8	9.3	9.9	7.2	7.1	5.8	6.3	8.3	10	8.7	8.5
368	United States	Mississippi	US28	9	5.4	7.2	0.0	0.0	7.8	6.6	5.4	8.9	9.6	8.1
369	United States	Missouri	US29	9.6	7.7	8.6	0.3	3.2	6.7	4.0	7.3	10	5.8	7.8
370	United States	Montana	US30	9.7	8.6	8.3	7.1	3.1	8.6	4.5	7.6	10	9.8	5.6
371	United States	Nebraska	US31	9.5	9.8	10.0	5.7	5.7	6.4	3.5	7.8	10	6	10
372	United States	Nevada	US32	8.7	6.8	8.3	0.9	4.1	8.3	2.6	7.9	7.8	8.1	6.3
373	United States	New Hampshire	US33	9.9	9.2	10.0	7.9	5.1	8.0	5.4	8.8	10	10	6.7
374	United States	New Jersey	US34	9.5	7.5	10.0	3.1	6.4	5.5	3.6	8.6	8.3	6.8	7.4
375	United States	New Mexico	US35	8.9	6.3	7.7	0.5	4.9	8.4	3.5	6.2	8.3	8.2	7.4
376	United States	New York	US36	9.2	7.0	10.0	5.2	6.9	5.4	2.8	8.1	7.2	8.4	7.4
377	United States	North Carolina	US37	9.2	6.9	8.1	2.6	3.7	7.3	5.2	7.3	9.4	7.7	8.5
378	United States	North Dakota	US38	9.9	10.0	10.0	7.1	4.3	7.2	4.0	7.9	10	6.4	10
379	United States	Ohio	US39	9.6	7.7	8.6	4.1	3.3	5.7	3.9	7.6	10	9.8	6.3
380	United States	Oklahoma	US40	9.1	7.5	8.9	2.1	1.1	7.3	1.3	6.7	8.9	7.8	7
381	United States	Oregon	US41	9.3	7.1	8.3	7.4	5.4	9.2	4.9	8.3	8.3	7.4	7
382	United States	Pennsylvania	US42	9.7	7.8	9.8	2.9	4.4	5.5	3.5	7.8	10	8.7	7
383	United States	Rhode Island	US44	9.3	7.3	10.0	5.9	4.5	8.0	3.7	8.4	8.9	9.9	8.9
384	United States	South Carolina	US45	9.2	6.7	7.6	0.2	2.6	8.0	4.2	6.8	10	5.2	9.6
385	United States	South Dakota	US46	9.5	9.5	10.0	6.7	5.4	7.0	3.4	7.4	10	9.9	7.4
386	United States	Tennessee	US47	9.4	6.7	8.6	2.2	1.7	6.9	2.1	6.8	9.4	8.8	8.5
387	United States	Texas	US48	8.5	7.6	9.4	3.4	4.5	7.7	1.6	7.2	7.2	6.9	8.1
388	United States	Utah	US49	9.5	8.4	7.7	7.9	6.0	9.1	2.4	8.5	7.2	9	7.4
389	United States	Vermont	US50	9.7	9.3	9.8	8.1	4.9	7.4	3.9	8.1	10	9.4	10
390	United States	Virginia	US51	9.6	8.0	10.0	4.3	5.1	5.9	4.8	8.1	9.4	7.7	8.5
391	United States	Washington	US53	9.5	7.3	10.0	6.9	6.1	8.8	4.4	8.4	8.3	8.9	7.8
392	United States	West Virginia	US54	9.5	6.2	7.5	5.2	0.7	6.5	0.2	6.8	10	8	6.3
393	United States	Wisconsin	US55	9.7	8.6	9.1	6.0	5.7	6.1	6.3	7.8	10	8.3	7.8
394	United States	Wyoming	US56	9.8	8.8	10.0	5.9	3.4	8.1	2.9	8.0	10	10	10

	Country	Education	Jobs	Income	Safety	Health	Environment	Civic engagement	Accessiblity to services	Housing	Community	Life satisfaction
0	Australia	7.862500	8.287500	7.075000	8.025000	7.712500	9.450000	9.800000	7.587500	7.837500	9.037500	8.362500
1	Austria	8.566667	8.066667	5.655556	9.711111	7.377778	3.266667	6.500000	7.866667	5.322222	8.477778	8.133333
2	Belgium	7.433333	4.600000	4.466667	7.233333	6.166667	2.466667	9.900000	7.966667	7.433333	7.966667	6.800000
3	Canada	6.900000	6.600000	5.623077	6.884615	5.884615	8.461538	5.361538	8.100000	8.146154	6.169231	6.830769
4	Chile	7.246667	6.786667	0.900000	5.480000	3.573333	7.900000	0.446667	1.153333	1.533333	4.686667	5.706667
5	Czech Republic	9.912500	7.525000	2.500000	7.887500	3.037500	1.900000	2.962500	7.337500	2.775000	6.562500	5.012500
6	Denmark	7.000000	8.080000	3.960000	9.680000	5.880000	6.080000	9.220000	8.640000	5.240000	9.700000	9.260000
7	Estonia TL3	9.240000	7.000000	1.420000	4.760000	1.640000	6.720000	3.500000	7.400000	2.160000	6.100000	1.260000
8	Finland	8.540000	7.400000	4.360000	9.720000	7.320000	7.980000	4.580000	8.520000	5.800000	7.080000	7.120000
9	France	7.818182	5.531818	4.895455	8.754545	8.431818	5.286364	8.227273	7.318182	5.268182	8.077273	5.818182
10	Germany	8.956250	8.150000	5.387500	9.087500	6.175000	3.962500	5.593750	8.818750	7.331250	8.862500	5.706250
11	Greece	5.484615	0.653846	1.723077	5.100000	7.900000	3.769231	4.030769	5.492308	3.346154	3.769231	1.407692
12	Hungary	8.728571	5.714286	1.785714	8.314286	0.442857	2.500000	3.342857	6.928571	1.114286	6.371429	0.157143
13	Iceland	5.900000	9.550000	3.700000	10.000000	8.000000	9.750000	8.150000	9.750000	3.900000	9.750000	7.750000
14	Ireland	7.550000	5.200000	4.450000	7.550000	6.850000	8.400000	5.500000	7.700000	6.650000	9.850000	7.400000
15	Israel	8.966667	7.400000	1.716667	8.333333	8.216667	0.583333	3.833333	6.283333	1.400000	6.700000	8.183333
16	Italy	5.447619	4.333333	4.347619	9.033333	9.133333	3.938095	6.814286	6.600000	4.723810	6.704762	3.947619
17	Japan	9.030000	9.710000	4.380000	9.570000	9.720000	4.360000	1.360000	6.260000	6.510000	7.150000	3.140000
18	Korea	7.528571	8.385714	3.457143	7.328571	8.428571	0.842857	6.885714	10.000000	2.857143	1.942857	2.900000
19	Luxembourg	8.100000	6.700000	7.100000	9.500000	8.100000	5.100000	10.000000	9.900000	6.100000	6.200000	6.700000
20	Mexico	1.168750	6.915625	0.165625	0.362500	1.450000	5.684375	3.737500	1.406250	0.453125	3.315625	7.450000
21	Netherlands	6.950000	7.741667	3.858333	9.533333	7.175000	3.983333	6.691667	9.916667	6.350000	8.883333	8.866667
22	New Zealand	6.257143	8.857143	3.435714	9.314286	5.750000	9.935714	6.578571	6.778571	7.507143	9.242857	8.300000
23	Norway	8.071429	9.285714	5.257143	9.428571	7.471429	8.857143	7.328571	8.857143	6.200000	8.714286	9.114286
24	Poland	9.731250	5.137500	2.256250	7.850000	2.243750	3.062500	0.693750	6.556250	0.537500	7.525000	2.850000
25	Portugal	1.857143	4.857143	3.557143	8.285714	5.500000	7.657143	1.714286	5.557143	5.500000	5.357143	0.685714
26	Slovak Republic	9.850000	5.050000	2.950000	8.250000	1.325000	2.350000	0.300000	7.475000	1.100000	7.025000	3.875000
27	Slovenia	9.000000	6.100000	3.050000	7.500000	6.450000	3.850000	1.300000	7.350000	2.800000	8.050000	3.550000
28	Spain	4.515789	1.878947	3.826316	9.542105	9.073684	6.263158	4.942105	6.921053	5.478947	8.752632	5.247368
29	Sweden	8.375000	8.150000	4.462500	9.462500	7.600000	7.925000	9.250000	8.750000	4.712500	8.125000	8.550000
30	Switzerland	8.528571	9.300000	5.114286	9.642857	9.257143	3.314286	0.600000	8.742857	4.257143	8.971429	9.085714
31	Turkey	0.807692	4.100000	0.619231	6.676923	2.961538	2.653846	9.415385	2.396154	0.642308	2.188462	1.253846
32	United Kingdom	7.866667	7.775000	4.875000	9.133333	6.725000	6.325000	4.400000	9.033333	6.408333	9.083333	6.575000
33	United States	9.421569	7.662745	9.025490	4.037255	4.350980	7.176471	3.811765	7.650980	8.988235	8.280392	7.743137