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In [1]:



    


#As always, we import everything
import os
import pandas as pd
import json
import folium
import math
%matplotlib inline
import matplotlib.pyplot as plt
from geopy.geocoders import Nominatim
import base64

We define a RESET constant, that allows the recreation of all graphs and all mappings. We will detail later in the notebook its use.





In [2]:



    


RESET = False

Exercise 1

First, let's get the data and put it in a dataframe. The .tsv file was found on the eurostat website. We had to modify the data so that it is better adapted for our analysis. First, in the dataset we found, there are entries for entities that are not countries and 2 countries that are not in Europe. We also changed the country code for Greece, as it was EL in this dataset, but usually GR is used. Moreover, this data lacked information for Switzerland so we added it manually. Using the other website with swiss statistics, we generated the unemployment rates averaged annually to match the data in this dataset. Then inserted them in the file manually.





In [3]:



    


eu_data = pd.read_table('data/tsdec450.tsv', na_values=': ')

Now, we can process it to get the id for each country.





In [4]:



    


eu_data['info'] = eu_data['info'].apply(lambda x: x.split(",")[3])
eu_data



    


















    
Out[4]:











  
    

      
      info
      1990
      1991
      1992
      1993
      1994
      1995
      1996
      1997
      1998
      ...
      2007
      2008
      2009
      2010
      2011
      2012
      2013
      2014
      2015
      2016
    

  
  
    

      0
      AT
      NaN
      NaN
      NaN
      NaN
      4.3
      4.2
      4.7
      4.7
      4.7
      ...
      4.9
      4.1
      5.3
      4.8
      4.6
      4.9
      5.4
      5.6
      5.7
      6.0
    

    

      1
      BE
      6.6
      6.4
      7.1
      8.6
      9.8
      9.7
      9.5
      9.2
      9.3
      ...
      7.5
      7.0
      7.9
      8.3
      7.2
      7.6
      8.4
      8.5
      8.5
      7.8
    

    

      2
      BG
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      ...
      6.9
      5.6
      6.8
      10.3
      11.3
      12.3
      13.0
      11.4
      9.2
      7.6
    

    

      3
      CY
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      ...
      3.9
      3.7
      5.4
      6.3
      7.9
      11.9
      15.9
      16.1
      15.0
      13.0
    

    

      4
      CZ
      NaN
      NaN
      NaN
      4.3
      4.3
      4.0
      3.9
      4.8
      6.5
      ...
      5.3
      4.4
      6.7
      7.3
      6.7
      7.0
      7.0
      6.1
      5.1
      4.0
    

    

      5
      DE
      NaN
      5.5
      6.6
      7.8
      8.4
      8.2
      8.9
      9.6
      9.4
      ...
      8.5
      7.4
      7.6
      7.0
      5.8
      5.4
      5.2
      5.0
      4.6
      4.1
    

    

      6
      DK
      7.2
      7.9
      8.6
      9.6
      7.7
      6.7
      6.3
      5.2
      4.9
      ...
      3.8
      3.4
      6.0
      7.5
      7.6
      7.5
      7.0
      6.6
      6.2
      6.2
    

    

      7
      EE
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      ...
      4.6
      5.5
      13.5
      16.7
      12.3
      10.0
      8.6
      7.4
      6.2
      6.8
    

    

      8
      GR
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      11.1
      ...
      8.4
      7.8
      9.6
      12.7
      17.9
      24.5
      27.5
      26.5
      24.9
      23.6
    

    

      9
      ES
      15.5
      15.5
      17.0
      20.8
      22.0
      20.7
      19.9
      18.4
      16.4
      ...
      8.2
      11.3
      17.9
      19.9
      21.4
      24.8
      26.1
      24.5
      22.1
      19.6
    

    

      10
      FI
      3.2
      6.6
      11.7
      16.3
      16.6
      15.4
      14.6
      12.7
      11.4
      ...
      6.9
      6.4
      8.2
      8.4
      7.8
      7.7
      8.2
      8.7
      9.4
      8.8
    

    

      11
      FR
      7.9
      8.1
      9.0
      10.1
      10.4
      10.2
      10.5
      10.7
      10.3
      ...
      8.0
      7.4
      9.1
      9.3
      9.2
      9.8
      10.3
      10.3
      10.4
      10.1
    

    

      12
      HR
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      ...
      9.9
      8.6
      9.3
      11.8
      13.7
      15.8
      17.4
      17.2
      16.1
      13.4
    

    

      13
      HU
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      9.9
      9.1
      8.7
      ...
      7.4
      7.8
      10.0
      11.2
      11.0
      11.0
      10.2
      7.7
      6.8
      5.1
    

    

      14
      IE
      13.4
      14.7
      15.4
      15.6
      14.3
      12.3
      11.7
      9.9
      7.5
      ...
      4.7
      6.4
      12.0
      13.9
      14.7
      14.7
      13.1
      11.3
      9.4
      7.9
    

    

      15
      IS
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      ...
      2.3
      3.0
      7.2
      7.6
      7.1
      6.0
      5.4
      5.0
      4.0
      3.0
    

    

      16
      IT
      8.9
      8.5
      8.8
      9.7
      10.6
      11.2
      11.2
      11.2
      11.3
      ...
      6.1
      6.7
      7.7
      8.4
      8.4
      10.7
      12.1
      12.7
      11.9
      11.7
    

    

      17
      LT
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      13.2
      ...
      4.3
      5.8
      13.8
      17.8
      15.4
      13.4
      11.8
      10.7
      9.1
      7.9
    

    

      18
      LU
      1.7
      1.6
      2.1
      2.6
      3.2
      2.9
      2.9
      2.7
      2.7
      ...
      4.2
      4.9
      5.1
      4.6
      4.8
      5.1
      5.9
      6.0
      6.5
      6.3
    

    

      19
      LV
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      ...
      6.1
      7.7
      17.5
      19.5
      16.2
      15.0
      11.9
      10.8
      9.9
      9.6
    

    

      20
      MT
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      ...
      6.5
      6.0
      6.9
      6.9
      6.4
      6.3
      6.4
      5.8
      5.4
      4.7
    

    

      21
      NL
      6.1
      5.7
      5.7
      6.5
      7.2
      8.3
      7.7
      6.5
      5.1
      ...
      4.2
      3.7
      4.4
      5.0
      5.0
      5.8
      7.3
      7.4
      6.9
      6.0
    

    

      22
      NO
      5.2
      5.5
      5.9
      6.0
      5.4
      4.9
      4.7
      3.9
      3.1
      ...
      2.5
      2.5
      3.2
      3.6
      3.3
      3.2
      3.5
      3.5
      4.4
      4.7
    

    

      23
      PL
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      10.9
      10.2
      ...
      9.6
      7.1
      8.1
      9.7
      9.7
      10.1
      10.3
      9.0
      7.5
      6.2
    

    

      24
      PT
      5.6
      5.0
      5.0
      6.3
      7.6
      7.9
      8.0
      7.5
      6.1
      ...
      9.1
      8.8
      10.7
      12.0
      12.9
      15.8
      16.4
      14.1
      12.6
      11.2
    

    

      25
      RO
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      6.1
      6.3
      ...
      6.4
      5.6
      6.5
      7.0
      7.2
      6.8
      7.1
      6.8
      6.8
      5.9
    

    

      26
      SE
      1.7
      3.1
      5.6
      9.1
      9.4
      8.8
      9.6
      9.9
      8.2
      ...
      6.1
      6.2
      8.3
      8.6
      7.8
      8.0
      8.0
      7.9
      7.4
      6.9
    

    

      27
      SI
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      6.9
      6.9
      7.4
      ...
      4.9
      4.4
      5.9
      7.3
      8.2
      8.9
      10.1
      9.7
      9.0
      8.0
    

    

      28
      SK
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      12.7
      ...
      11.2
      9.6
      12.1
      14.5
      13.7
      14.0
      14.2
      13.2
      11.5
      9.7
    

    

      29
      TR
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      ...
      9.1
      10.0
      13.0
      11.1
      9.1
      8.4
      9.0
      9.9
      10.3
      10.9
    

    

      30
      GB
      6.9
      8.6
      9.8
      10.2
      9.3
      8.5
      7.9
      6.8
      6.1
      ...
      5.3
      5.6
      7.6
      7.8
      8.1
      7.9
      7.5
      6.1
      5.3
      4.8
    

    

      31
      CH
      NaN
      NaN
      NaN
      4.5
      4.7
      4.2
      4.7
      5.2
      3.9
      ...
      2.8
      2.6
      3.7
      3.5
      2.8
      2.9
      3.2
      3.0
      3.2
      3.3
    

  



32 rows × 28 columns

Now we can define a function that returns the color of a country based on its unemployment rate. For this, we need to choose a palette of colors : we used colorbrewer to get one which is color-blind friendly. To get the color for a country, we simply bin all the unemployment_rate into one of the colors.

We decided to use a linear scale from the lowest unemployment value to the highest in our data.





In [5]:



    


colors_unemployement = ['#fef0d9','#fdd49e','#fdbb84','#fc8d59','#e34a33','#b30000']
min_unemployement_eu = eu_data['2016'].min()
max_unemployement_eu = eu_data['2016'].max()

def get_color_eu(country, properties):  
    values = eu_data.loc[eu_data['info'] == country, '2016'].values
    if len(values) == 0:
        return '#000000'
    
    unemployement_rate = values[0]
    ratio = (unemployement_rate - min_unemployement_eu) / (max_unemployement_eu - min_unemployement_eu)
    index = math.floor(ratio * len(colors_unemployement))
    if index == len(colors_unemployement):
        index = index - 1
    return colors_unemployement[index]

We also define a function that checks if a country is in the dataframe, and if not, we make its overly transparent.





In [6]:



    


def get_opacity_eu(country):
    values = eu_data.loc[eu_data['info'] == country, '2016'].values
    return 1 if len(values) > 0 else 0

Now we can create the Europe map, and add the overlay, using the previous functions.

The second map is only used to display the key. We used to following website to convert the topojson file to gejson https://jeffpaine.github.io/geojson-topojson/

Note that we cannot use only the choropleth map since there are countries for which we do not have the data. We'd like these countries to be transparent so we only use that map for the key.





In [7]:



    


map_eu = folium.Map([51,15], tiles='cartodbpositron', zoom_start=4)


# Color of the country
folium.TopoJson(
    open('topojson/europe.topojson.json'),
    object_path='objects.europe',
    style_function=lambda feature: {
        'fillOpacity' : get_opacity_eu(feature['id']), #opacity for the fill color
        'opacity' : get_opacity_eu(feature['id']), #opacity for the borders
        'fillColor': get_color_eu(feature['id'], feature['properties']),
        'color' : 'black',
        'weight' : 1
        }
).add_to(map_eu)
 
map_eu.choropleth(geo_data='topojson/europe.geojson.json',data=eu_data,
             columns=['info', '2016'],
             key_on='feature.id',
             fill_color='OrRd',
             fill_opacity=0, 
             line_opacity=0.0,
             legend_name='Percentage of unemployment in country')

For interactivity, we decided to place, for each country, a popup displaying the graph of unemployment from 1990 to 2016. Folium provides a way to add a marker, that when clicked on, displays a popup.

Thus, we first need to get, for each country its center to place the marker at the right coordinates. We used the geopy library to do this.

As this operation is quite slow, and not 100% accurate for each country, we decided to create a mapping using the library, then save it to a JSON, and finally to manually edit this JSON if the coordinates were not accurate. When this was done, we only need to reload this JSON and not recreate it each time the notebook is run.

The file containing the mapping between country ID and name was generated beforehand as well but we choose not to regenerate it ever because at this point in the program, it's not possible to efficiently obtain it. We will need it to display country names related to their code.





In [8]:



    


with open('id_country_mapping.json', 'r') as infile:
    id_country_mapping = json.load(infile)
if RESET:
    position_mapping = {}
    geolocator = Nominatim()
    for country_id in eu_data['info']:
        if country_id in id_country_mapping:
            location = geolocator.geocode(id_country_mapping[country_id])
            if (location):
                position_mapping[country_id] = [location.latitude,location.longitude]

    with open('position_mapping.json', 'w') as outfile:
        json.dump(position_mapping, outfile)
else:
    with open('position_mapping.json', 'r') as infile:
        position_mapping = json.load(infile)

Now that we have the positions, for each country we create a graph and save it. We used the previously defined id_country_mapping to get the full name of each country.





In [9]:



    


if RESET:
    for index, row in eu_data.iterrows():
        global id_country_mapping

        if row.values[0] in id_country_mapping:
            row_values = row.iloc[1:].astype(float)
            plot = row_values.transpose().plot()
            plt.ylim(0,30)
            plt.xlabel('Year')
            plt.ylabel('% of unemployed')
            plt.title('Graph of unemployment in ' + id_country_mapping[row.values[0]])
            plt.savefig('eu_graphs/graph_' + row.values[0] + '.png')
            plt.close()

We decided to host all the graphs on our personal server, in order to keep the maps at a low size.

Now that we have both the graphs and the positions, we can, for each country, place a custom marker (the country's flag) that displays the corresponding graph when clicked.

You can see the map here :

https://nbviewer.jupyter.org/github/RadoslawDryzner/LeRepoDuGuerrier/blob/master/Homework03/map_eu.ipynb





In [10]:



    


all_markers = folium.FeatureGroup("Markers")
for country_id in eu_data['info']:
    if country_id in position_mapping:
        location = position_mapping[country_id]
        if location:
            html = '<img src="http://feudal-ambitions.com/ADA/HW3/eu_graphs/graph_' + country_id + '" width=450 height=300>'
            iframe = folium.IFrame(html, width=470, height=320)
            popup = folium.Popup(iframe, max_width=2650)

            custom_icon = folium.features.CustomIcon("http://feudal-ambitions.com/ADA/HW3/eu_flags/" + country_id.lower() + ".png", icon_size=(25, 25))
            all_markers.add_child(folium.Marker(location, popup=popup, icon=custom_icon))


map_eu.add_child(all_markers)
map_eu



    


















    
Out[10]:

In this map, we can see that southern and western european countries have the highest unemployment on the continent. Some of these countries such as Greece or Spain experienced financial crises in recent times which explain these large number. Moreover, thanks to our interactive graphs, we can visualise the evolution of many countries such as Poland which today has a low unemployment rate but historically exprienced very high unemployment.

When it comes to Switzerland, we can see that it's in the lower unemployment bracket. In fact, Swiss unemployment rate is among the lowest and has been historically very low, even during crises that touched most other European countries.

Exercise 2

For this exercise, we generate a .csv file from the amstat swiss statistics website. The file we get from the site is the one that shows unemployment rates. These rates are defined on the website as the number of unemployed people divided by the active population of the canton scaled by 100. We modified the file manually to remove some useless title columns and lines and changed the names of the cantons into their codes.





In [11]:



    


ch_data = pd.read_csv('data/unemployed_switzerland.csv')



    











In [12]:



    


ch_data



    


















    
Out[12]:











  
    

      
      Canton
      1993
      1994
      1995
      1996
      1997
      1998
      1999
      2000
      2001
      ...
      2008
      2009
      2010
      2011
      2012
      2013
      2014
      2015
      2016
      2017
    

  
  
    

      0
      ZH
      3.5
      4.7
      4.2
      4.0
      5.3
      5.1
      3.6
      2.2
      1.7
      ...
      2.7
      3.0
      4.1
      3.4
      3.0
      3.4
      3.4
      3.5
      3.8
      3.9
    

    

      1
      BE
      3.4
      4.5
      3.9
      3.7
      4.9
      4.0
      2.5
      1.8
      1.3
      ...
      2.0
      2.3
      3.1
      2.6
      2.2
      2.5
      2.6
      2.6
      2.9
      3.0
    

    

      2
      LU
      3.0
      4.0
      3.4
      3.7
      4.8
      4.0
      2.6
      1.6
      1.3
      ...
      2.2
      2.6
      3.0
      2.1
      2.1
      2.2
      2.1
      2.1
      2.3
      2.2
    

    

      3
      UR
      1.7
      2.2
      1.9
      2.4
      3.5
      2.4
      1.5
      0.9
      0.6
      ...
      1.0
      1.3
      1.8
      1.5
      1.7
      1.7
      1.5
      1.5
      1.3
      1.5
    

    

      4
      SZ
      2.1
      2.8
      2.1
      2.7
      3.8
      3.0
      2.1
      1.0
      0.6
      ...
      1.5
      1.8
      2.3
      1.8
      1.6
      1.6
      1.8
      1.7
      2.0
      2.0
    

    

      5
      OW
      1.7
      2.2
      1.9
      2.1
      2.8
      1.9
      0.8
      0.4
      0.5
      ...
      1.2
      1.5
      1.6
      0.9
      0.9
      0.9
      1.1
      0.9
      1.0
      1.0
    

    

      6
      NW
      3.3
      3.2
      2.4
      2.4
      3.3
      2.2
      1.1
      0.5
      0.5
      ...
      1.2
      1.6
      2.0
      1.2
      0.9
      1.0
      1.1
      1.2
      1.3
      1.3
    

    

      7
      GL
      2.0
      2.5
      1.8
      3.0
      3.9
      3.2
      2.0
      1.5
      0.8
      ...
      1.5
      2.0
      3.0
      2.3
      2.4
      2.8
      2.7
      2.2
      2.6
      2.5
    

    

      8
      ZG
      3.6
      4.2
      3.2
      3.1
      4.2
      3.6
      2.8
      1.5
      1.1
      ...
      1.8
      2.4
      3.0
      2.2
      1.9
      2.2
      2.4
      2.3
      2.5
      2.6
    

    

      9
      FR
      4.1
      6.2
      5.8
      5.2
      5.5
      4.9
      3.4
      2.1
      1.5
      ...
      2.9
      3.4
      3.3
      2.6
      2.6
      3.1
      3.1
      3.1
      3.4
      3.2
    

    

      10
      SO
      4.0
      5.0
      3.8
      4.0
      5.9
      5.7
      3.1
      2.2
      1.5
      ...
      2.5
      3.0
      4.3
      3.0
      2.5
      3.1
      2.8
      2.6
      3.2
      3.2
    

    

      11
      BS
      4.9
      6.3
      5.5
      4.7
      5.3
      4.8
      3.1
      2.4
      2.2
      ...
      3.1
      3.5
      4.7
      4.3
      3.8
      4.1
      3.7
      3.9
      4.0
      4.2
    

    

      12
      BL
      2.9
      3.9
      3.4
      3.2
      3.9
      3.6
      2.1
      1.6
      1.3
      ...
      2.4
      2.7
      3.7
      3.2
      2.9
      2.9
      2.8
      2.8
      3.1
      3.1
    

    

      13
      SH
      3.6
      4.9
      4.2
      4.4
      5.6
      5.6
      3.5
      2.3
      1.9
      ...
      2.5
      2.5
      4.0
      2.7
      2.5
      3.0
      3.1
      3.3
      3.5
      3.6
    

    

      14
      AR
      2.4
      3.1
      2.1
      2.1
      2.7
      2.4
      1.2
      1.0
      1.0
      ...
      1.5
      1.9
      2.3
      1.9
      1.5
      1.8
      1.8
      2.0
      2.1
      2.0
    

    

      15
      AI
      1.6
      1.7
      0.9
      1.3
      2.0
      1.6
      0.6
      0.5
      0.3
      ...
      1.1
      1.2
      1.4
      1.0
      1.2
      1.7
      1.4
      1.3
      1.3
      1.4
    

    

      16
      SG
      2.9
      3.7
      3.0
      3.1
      4.5
      3.6
      2.6
      1.8
      1.3
      ...
      1.9
      2.6
      3.6
      2.7
      2.4
      2.8
      2.6
      2.4
      2.8
      2.7
    

    

      17
      GR
      1.6
      2.2
      2.2
      2.6
      3.2
      3.5
      2.3
      1.2
      1.2
      ...
      1.3
      1.7
      1.8
      1.8
      1.8
      2.0
      2.0
      2.0
      2.1
      1.9
    

    

      18
      AG
      2.8
      3.7
      3.0
      3.4
      4.9
      3.8
      2.7
      1.7
      1.3
      ...
      2.5
      2.9
      3.6
      3.0
      2.9
      3.1
      3.1
      3.1
      3.4
      3.5
    

    

      19
      TG
      2.4
      3.3
      2.8
      3.2
      4.5
      4.4
      2.8
      1.6
      1.2
      ...
      2.1
      2.6
      3.5
      2.6
      2.2
      2.7
      2.7
      2.5
      2.9
      2.6
    

    

      20
      TI
      6.2
      7.8
      7.4
      8.5
      9.2
      8.9
      6.4
      4.7
      3.5
      ...
      4.9
      5.1
      5.4
      5.4
      4.9
      5.2
      4.9
      4.6
      4.2
      4.0
    

    

      21
      VD
      6.0
      8.1
      7.7
      7.3
      8.3
      6.9
      5.1
      3.6
      2.9
      ...
      4.1
      4.6
      5.5
      5.1
      4.9
      5.3
      5.1
      4.9
      5.1
      5.2
    

    

      22
      VS
      6.0
      8.3
      8.3
      7.7
      8.9
      7.5
      5.6
      3.9
      3.0
      ...
      4.2
      4.8
      5.1
      4.5
      4.5
      5.3
      5.2
      5.4
      5.5
      5.2
    

    

      23
      NE
      5.8
      7.0
      6.4
      5.8
      6.3
      6.4
      4.9
      3.3
      2.1
      ...
      3.6
      4.3
      7.1
      5.9
      4.8
      5.5
      5.6
      5.6
      6.1
      6.6
    

    

      24
      GE
      6.2
      8.0
      7.8
      6.9
      7.8
      7.4
      5.6
      4.6
      3.9
      ...
      5.9
      6.2
      6.9
      6.5
      5.2
      5.5
      5.7
      5.5
      5.8
      5.7
    

    

      25
      JU
      5.0
      7.1
      5.7
      6.1
      7.4
      6.5
      3.7
      2.7
      1.8
      ...
      3.2
      4.0
      6.0
      4.7
      3.5
      4.0
      4.1
      4.2
      4.8
      5.3
    

  



26 rows × 26 columns

We define similar functions for constructing the canton mappings to IDs and their location. Everything is almost the same as for EU countries. Like for Europe we generate a geojson to display the choropleth map. For the markers on the Swiss map, we put each canton's coat-of-arms.





In [13]:



    


colors_unemployement = ['#fef0d9','#fdd49e','#fdbb84','#fc8d59','#e34a33','#b30000']



    











In [14]:



    


map_ch = folium.Map([46.8,8.3], tiles='cartodbpositron', zoom_start=8)

map_ch.choropleth(geo_data='topojson/ch-cantons.geojson.json',data=ch_data,
             columns=['Canton', '2017'],
             key_on='feature.id',
             fill_color='OrRd',
             fill_opacity=1, 
             line_opacity=1,
             legend_name='Percentage of unemployment in canton')



    











In [15]:



    


with open('id_canton_mapping.json', 'r') as infile:
    id_canton_mapping = json.load(infile)
if RESET:
    position_canton_mapping = {}
    geolocator = Nominatim()
    for canton_id in id_canton_mapping:
        location = geolocator.geocode(id_canton_mapping[canton_id])
        if (location):
            position_canton_mapping[canton_id] = [location.latitude,location.longitude]

    with open('position_canton_mapping.json', 'w') as outfile:
        json.dump(position_canton_mapping, outfile)
else:
    with open('position_canton_mapping.json', 'r') as infile:
        position_canton_mapping = json.load(infile)



    











In [16]:



    


if RESET:
    for index, row in ch_data.iterrows():
        global id_canton_mapping

        if row.values[0] in id_canton_mapping:
            row_values = row.iloc[1:].astype(float)
            plot = row_values.transpose().plot()
            plt.ylim(0,30)
            plt.xlabel('Year')
            plt.ylabel('% of unemployed')
            plt.title('Graph of unemployment in ' + id_canton_mapping[row.values[0]])
            plt.savefig('ch_graphs/graph_' + row.values[0] + '.png')
            plt.close()

You can see the map here :

https://nbviewer.jupyter.org/github/RadoslawDryzner/LeRepoDuGuerrier/blob/master/Homework03/map_ch.ipynb





In [17]:



    


all_markers = folium.FeatureGroup("Markers")
for canton_id in id_canton_mapping:
    location = position_canton_mapping[canton_id]
    
    html = '<img src="http://feudal-ambitions.com/ADA/HW3/ch_graphs/graph_' + canton_id + '" width=450 height=300>'
    iframe = folium.IFrame(html, width=470, height=320)
    popup = folium.Popup(iframe, max_width=2650)

    custom_icon = folium.features.CustomIcon("http://feudal-ambitions.com/ADA/HW3/ch_flags/" + canton_id + ".png", icon_size=(25, 30))
    all_markers.add_child(folium.Marker(location, popup=popup, icon=custom_icon))
        


map_ch.add_child(all_markers)
map_ch



    


















    
Out[17]:

As before, we added unemployment evolution graphs for interactivity.

We can note on this map that western regions seem to have higher unemplyment rates than eastern regions. As a general rule, the cantons of the french speaking Romandie seem to experience higher unemployment rates. Zurich, which houses the largest city of the country also has heightened unemployment rate. The cantons that have the least unemployment are mostly in the inner or mountainous Swiss regions and all have low, rural populations.

Now we would like to view the statistics containing the jobseekers that are currently employed. Unfortunetely, the website does not provide the rate of all jobseekers in the population. So we calculate these rates ourselves.

First we take the numbers of all jobseekers and the number of jobless jobseekers (not the rates). Using the number of unemployed people and the rates from before, we calculate the active population of the cantons. Then we can calculate the rate of all jobseekers (employer or not) in the canton.





In [18]:



    


ch_data2 = pd.read_csv('data/unemployed_num_switzerland.csv', thousands="'")
ch_data3 = pd.read_csv('data/jobseekers_switzerland.csv', thousands="'")
ch_data4 = pd.read_csv('data/unemployed_foreigner_swiss.csv', thousands="'")



    











In [19]:



    


active_pop = ((ch_data2.iloc[:, 1:] * 100) / ch_data.iloc[:, 1:])
ch_jobseeker_rate = (ch_data3.iloc[:, 1:] * 100) / active_pop
ch_jobseeker_rate = pd.concat((ch_data['Canton'], ch_jobseeker_rate), axis=1)
ch_jobseeker_rate



    


















    
Out[19]:











  
    

      
      Canton
      1993
      1994
      1995
      1996
      1997
      1998
      1999
      2000
      2001
      ...
      2008
      2009
      2010
      2011
      2012
      2013
      2014
      2015
      2016
      2017
    

  
  
    

      0
      ZH
      3.543061
      4.847424
      4.372379
      4.655738
      6.022974
      6.475108
      5.381440
      3.386926
      2.469623
      ...
      3.651884
      3.832379
      4.888359
      4.252009
      3.689619
      4.131530
      4.120756
      4.226515
      4.565434
      4.737271
    

    

      1
      BE
      3.517128
      4.804882
      4.280949
      4.339431
      5.871509
      5.467204
      4.438869
      3.159725
      2.075281
      ...
      3.059690
      3.249269
      4.249063
      3.713002
      3.117606
      3.319684
      3.439394
      3.454268
      3.699776
      3.819630
    

    

      2
      LU
      3.025438
      4.158790
      3.679684
      4.009896
      5.289272
      5.232825
      4.126214
      2.542723
      2.036595
      ...
      3.367720
      3.812309
      4.378942
      3.573684
      3.225182
      3.413239
      3.230804
      3.198715
      3.517254
      3.508526
    

    

      3
      UR
      1.700000
      2.341341
      2.063036
      2.737500
      4.173077
      3.715736
      2.607143
      1.676471
      1.121212
      ...
      1.748571
      2.121973
      2.805263
      2.405556
      2.767812
      2.720000
      2.286207
      2.363014
      2.234375
      2.343434
    

    

      4
      SZ
      2.111713
      2.993910
      2.444853
      3.030438
      4.498792
      4.787234
      3.795830
      2.158389
      1.264615
      ...
      2.514870
      2.950312
      3.497541
      2.869084
      2.410233
      2.477382
      2.709160
      2.540217
      2.834600
      2.877369
    

    

      5
      OW
      1.728452
      2.291374
      1.975725
      2.219398
      3.211765
      2.735165
      1.940000
      1.188060
      1.403614
      ...
      2.616667
      2.358779
      2.605310
      1.721538
      1.804762
      1.781347
      1.903320
      1.735714
      1.850679
      1.741935
    

    

      6
      NW
      3.300000
      3.421091
      2.496682
      2.503103
      3.539378
      3.019948
      2.488525
      1.426316
      1.630208
      ...
      2.666667
      2.576687
      3.054705
      2.217021
      1.678037
      1.913223
      1.918889
      2.173665
      2.230392
      2.280328
    

    

      7
      GL
      2.035806
      2.596349
      1.849180
      3.262185
      4.326175
      4.387421
      3.368159
      2.531546
      1.428571
      ...
      3.355519
      3.781095
      5.090639
      4.408333
      3.696089
      4.347011
      4.102427
      3.569565
      3.984300
      3.862434
    

    

      8
      ZG
      3.633823
      4.336228
      3.291550
      3.467655
      4.744444
      4.393782
      4.003620
      2.119710
      1.791228
      ...
      3.063939
      3.635897
      4.404624
      3.673496
      3.212700
      3.680315
      3.835221
      3.777720
      4.025148
      4.191686
    

    

      9
      FR
      4.194878
      6.479762
      6.352161
      6.686509
      8.277768
      8.190906
      6.804697
      4.580595
      3.360390
      ...
      4.650493
      5.367301
      5.357220
      4.557858
      4.446928
      5.207973
      5.049659
      5.027670
      5.514398
      5.643413
    

    

      10
      SO
      4.024351
      5.060775
      3.928911
      4.213125
      6.147207
      6.001173
      4.986670
      3.585886
      2.507014
      ...
      4.027652
      4.278713
      5.760072
      4.514736
      3.930047
      4.548155
      4.281043
      3.940603
      4.804145
      4.930185
    

    

      11
      BS
      5.109543
      6.639031
      5.925323
      5.485455
      6.402801
      6.138855
      4.523725
      3.769058
      3.421106
      ...
      4.482596
      5.104938
      6.532745
      6.129583
      5.401723
      5.788736
      5.170436
      5.432468
      5.608310
      5.981057
    

    

      12
      BL
      2.990097
      4.161402
      3.726205
      3.512448
      4.454258
      4.471139
      3.500750
      2.671852
      2.093351
      ...
      3.280000
      3.543885
      4.510288
      4.101744
      3.725998
      3.803043
      3.608638
      3.627056
      3.993106
      4.066130
    

    

      13
      SH
      3.925839
      5.994227
      5.799231
      5.615894
      6.831524
      7.206887
      5.468093
      4.096480
      3.584667
      ...
      4.630567
      4.301619
      6.494598
      5.156266
      4.805849
      5.109489
      5.422157
      5.509903
      6.043182
      6.018147
    

    

      14
      AR
      2.400000
      3.208772
      2.165625
      2.194595
      3.080669
      2.851185
      2.062500
      1.503597
      1.517123
      ...
      2.125592
      2.482482
      3.605961
      3.267450
      2.893333
      3.017647
      2.799288
      3.093023
      3.311924
      3.206557
    

    

      15
      AI
      1.600000
      1.714530
      0.943548
      1.314444
      2.201439
      2.285714
      1.258537
      0.775000
      0.600000
      ...
      1.709639
      1.741935
      2.005085
      1.453488
      1.618868
      2.274483
      1.966667
      1.777778
      1.669828
      1.855814
    

    

      16
      SG
      3.020253
      3.868099
      3.222018
      3.471150
      5.101528
      5.106267
      4.203287
      2.858366
      2.161508
      ...
      3.200242
      3.946421
      5.057612
      4.204372
      3.781188
      4.131849
      4.089745
      3.894231
      4.439329
      4.243162
    

    

      17
      GR
      1.622284
      2.444566
      2.340568
      2.775446
      3.519110
      4.299968
      3.728326
      2.401891
      2.219549
      ...
      2.664662
      3.100711
      3.148879
      3.264987
      3.120975
      3.353309
      3.447687
      3.395519
      3.666972
      3.464336
    

    

      18
      AG
      2.856103
      3.813779
      3.137931
      3.519983
      5.088768
      5.334089
      4.442834
      2.887635
      2.119823
      ...
      3.611407
      3.990320
      4.781631
      4.265589
      3.941204
      4.172546
      4.141513
      4.118205
      4.531492
      4.724529
    

    

      19
      TG
      2.409234
      3.551331
      3.060007
      3.529370
      4.995046
      5.350148
      4.174545
      2.566650
      1.971771
      ...
      3.320702
      3.709312
      4.644015
      3.834222
      3.206013
      3.814830
      3.807777
      3.720577
      4.380083
      4.177565
    

    

      20
      TI
      6.653606
      8.865519
      9.267670
      10.922964
      12.933840
      13.284955
      10.386335
      7.605025
      5.775614
      ...
      6.869880
      6.947944
      7.421866
      7.466044
      6.942133
      7.321215
      6.981327
      6.743223
      6.307089
      6.167826
    

    

      21
      VD
      6.164168
      8.587006
      8.596616
      9.419059
      10.674620
      10.140381
      7.794234
      5.490156
      4.518626
      ...
      5.646355
      6.125189
      7.011516
      6.727058
      6.389721
      6.870973
      6.634387
      6.461000
      6.821874
      7.048123
    

    

      22
      VS
      6.092295
      8.379713
      8.467644
      9.607070
      11.531091
      11.907687
      9.883640
      6.982548
      5.497932
      ...
      7.011041
      7.494942
      7.439553
      6.997344
      6.967762
      7.949380
      7.711341
      7.666731
      7.876522
      7.455878
    

    

      23
      NE
      5.963414
      8.278220
      7.885037
      7.737658
      8.537206
      8.877078
      7.843500
      5.607308
      3.366262
      ...
      5.503302
      6.001099
      9.270291
      8.219534
      6.759001
      7.189126
      7.582224
      7.396999
      7.896339
      8.410311
    

    

      24
      GE
      6.238616
      8.090732
      7.883246
      9.097760
      10.089761
      10.150841
      8.916381
      7.216715
      6.008992
      ...
      8.607553
      8.564248
      9.479232
      8.863636
      6.586638
      6.675467
      6.868288
      6.817063
      7.168807
      7.275575
    

    

      25
      JU
      5.033951
      7.256078
      5.870787
      6.319625
      7.702419
      7.190238
      5.929761
      4.371288
      3.041068
      ...
      4.885896
      5.770065
      8.190210
      6.977348
      5.200565
      5.814116
      5.809914
      5.886507
      6.516034
      7.175596
    

  



26 rows × 26 columns

As before, we redefine the necessary functions with small changes and plot our map.





In [20]:



    


colors_unemployement = ['#fef0d9','#fdd49e','#fdbb84','#fc8d59','#e34a33','#b30000']



    











In [21]:



    


map_ch_j = folium.Map([46.8,8.3], tiles='cartodbpositron', zoom_start=8)

map_ch_j.choropleth(geo_data='topojson/ch-cantons.geojson.json',data=ch_jobseeker_rate,
             columns=['Canton', '2017'],
             key_on='feature.id',
             fill_color='OrRd',
             fill_opacity=1, 
             line_opacity=1,
             legend_name='Percentage of jobseekers in canton')



    











In [22]:



    


if RESET:
    for index, row in ch_jobseeker_rate.iterrows():
        global id_canton_mapping
        row_values = row.iloc[1:].astype(float)
        plot = row_values.transpose().plot()
        plt.ylim(0,30)
        plt.xlabel('Year')
        plt.ylabel('% of jobseekers')
        plt.title('Graph of jobseekers in ' + id_canton_mapping[row.values[0]])
        plt.savefig('ch_graphs/graph_j_' + row.values[0] + '.png')
        plt.close()

You can see the map here :

https://nbviewer.jupyter.org/github/RadoslawDryzner/LeRepoDuGuerrier/blob/master/Homework03/map_jobseekers_ch.ipynb





In [23]:



    


all_markersj = folium.FeatureGroup("Markers")
for canton_id in id_canton_mapping:
    location = position_canton_mapping[canton_id]
    
    html = '<img src="http://feudal-ambitions.com/ADA/HW3/ch_graphs/graph_j_' + canton_id + '" width=450 height=300>'
    iframe = folium.IFrame(html, width=470, height=320)
    popup = folium.Popup(iframe, max_width=2650)

    custom_icon = folium.features.CustomIcon("http://feudal-ambitions.com/ADA/HW3/ch_flags/" + canton_id + ".png", icon_size=(25, 30))
    all_markersj.add_child(folium.Marker(location, popup=popup, icon=custom_icon))

map_ch_j.add_child(all_markersj)
map_ch_j



    


















    
Out[23]:

In this case we see that in general the rates are higher which makes sense since we add to unemployed people the employed jobseekers. Moreover we see that Romandie still has the highest rates although this can be explained by the original high unemployment figures. We can see some changes in how the cantons are ranked in the rates like Valais for example.

Exercice 3

For this exercise, we again use the amstat swiss statistics website to generate data that we need. This time we generate unemployment numbers separated by nationality and later further separated by age.





In [24]:



    


ch_data4
ch_foreigner = ch_data4[ch_data4.Nationalité == 'Etrangers']
ch_foreigner.index = range(0,len(ch_foreigner))
ch_foreigner = ch_foreigner.drop('Nationalité', axis=1)
ch_swiss = ch_data4[ch_data4.Nationalité == 'Suisses']
ch_swiss.index = range(0,len(ch_swiss))
ch_swiss = ch_swiss.drop('Nationalité', axis=1)



    











In [25]:



    


ch_swiss_rate = (ch_swiss.iloc[:, 1:] * 100) / active_pop
ch_swiss_rate = pd.concat((ch_data['Canton'], ch_swiss_rate), axis=1)
ch_foreigner_rate = (ch_foreigner.iloc[:, 1:] * 100) / active_pop
ch_foreigner_rate = pd.concat((ch_data['Canton'], ch_foreigner_rate), axis=1)



    











In [26]:



    


ch_swiss



    


















    
Out[26]:











  
    

      
      Canton
      1993
      1994
      1995
      1996
      1997
      1998
      1999
      2000
      2001
      ...
      2008
      2009
      2010
      2011
      2012
      2013
      2014
      2015
      2016
      2017
    

  
  
    

      0
      ZH
      13860
      18463
      15721
      13929
      18376
      16693
      11301
      7665
      6293
      ...
      10780
      12276
      18420
      15053
      13508
      14449
      15355
      15251
      16658
      17327
    

    

      1
      BE
      11454
      15289
      12692
      11632
      15291
      12155
      7158
      5371
      4113
      ...
      6724
      7759
      10977
      9195
      7627
      8568
      9279
      9212
      9980
      10284
    

    

      2
      LU
      3386
      4211
      3324
      3378
      4308
      3515
      2188
      1443
      1236
      ...
      2425
      2788
      3793
      2580
      2560
      2575
      2653
      2485
      2876
      2764
    

    

      3
      UR
      198
      214
      186
      233
      378
      233
      147
      92
      50
      ...
      110
      112
      208
      153
      157
      167
      133
      119
      112
      122
    

    

      4
      SZ
      727
      922
      655
      835
      1148
      796
      600
      382
      258
      ...
      633
      694
      1049
      811
      693
      727
      822
      762
      881
      959
    

    

      5
      OW
      174
      203
      161
      180
      262
      176
      74
      41
      52
      ...
      128
      152
      195
      116
      99
      103
      145
      106
      109
      114
    

    

      6
      NW
      440
      404
      326
      306
      411
      259
      123
      64
      69
      ...
      182
      217
      322
      192
      141
      159
      158
      168
      186
      172
    

    

      7
      GL
      245
      273
      207
      275
      330
      295
      191
      165
      86
      ...
      183
      216
      387
      279
      301
      313
      325
      242
      265
      284
    

    

      8
      ZG
      1046
      1169
      808
      772
      1072
      923
      726
      508
      326
      ...
      597
      803
      1056
      799
      684
      783
      866
      811
      915
      929
    

    

      9
      FR
      3268
      4829
      4427
      3639
      3653
      3147
      2051
      1480
      1061
      ...
      2023
      2275
      2723
      2152
      2031
      2250
      2529
      2497
      2804
      2495
    

    

      10
      SO
      3007
      3791
      2840
      2711
      3887
      3709
      1961
      1476
      887
      ...
      1823
      2181
      3432
      2513
      2127
      2571
      2274
      2132
      2539
      2590
    

    

      11
      BS
      2856
      3481
      2877
      2274
      2582
      2263
      1501
      1129
      1068
      ...
      1607
      1694
      2193
      1966
      1775
      1889
      1725
      1760
      1760
      1849
    

    

      12
      BL
      2093
      3012
      2563
      2423
      2869
      2514
      1594
      1365
      1106
      ...
      2037
      2305
      3375
      2935
      2557
      2591
      2558
      2418
      2670
      2706
    

    

      13
      SH
      781
      1015
      768
      816
      971
      946
      550
      376
      331
      ...
      522
      521
      892
      605
      581
      679
      680
      677
      744
      773
    

    

      14
      AR
      367
      470
      332
      288
      360
      356
      184
      152
      202
      ...
      285
      335
      433
      345
      279
      321
      345
      366
      363
      379
    

    

      15
      AI
      62
      68
      37
      51
      72
      59
      25
      23
      13
      ...
      47
      60
      76
      54
      58
      86
      69
      71
      55
      58
    

    

      16
      SG
      3559
      4637
      3764
      3484
      4816
      3643
      2547
      2018
      1570
      ...
      2447
      3191
      4908
      3724
      3376
      3680
      3552
      3302
      3819
      3752
    

    

      17
      GR
      976
      1377
      1315
      1485
      1861
      1969
      1187
      727
      707
      ...
      694
      860
      1004
      938
      877
      915
      919
      883
      958
      875
    

    

      18
      AG
      4428
      5843
      4449
      4915
      6905
      4994
      3499
      2488
      1937
      ...
      4125
      4701
      6886
      5706
      5273
      5753
      5822
      5821
      6372
      6311
    

    

      19
      TG
      1507
      1942
      1546
      1644
      2325
      2303
      1350
      1018
      858
      ...
      1481
      1767
      2790
      2038
      1781
      1996
      2066
      1848
      2145
      1917
    

    

      20
      TI
      4583
      5597
      5176
      5702
      5993
      5856
      4231
      3297
      2406
      ...
      3785
      3774
      4452
      4430
      3866
      4039
      3913
      3802
      3578
      3452
    

    

      21
      VD
      11336
      15119
      14013
      12727
      13995
      11412
      8698
      6174
      5024
      ...
      7053
      7718
      10328
      9496
      8931
      9360
      9296
      9047
      9467
      9749
    

    

      22
      VS
      4755
      6462
      6529
      5815
      6400
      5340
      3830
      2901
      2285
      ...
      2665
      2845
      3566
      3190
      2859
      3375
      3562
      3776
      4050
      3716
    

    

      23
      NE
      3085
      3663
      3247
      2845
      3071
      3103
      2288
      1508
      1053
      ...
      1741
      1988
      3371
      2887
      2323
      2531
      2563
      2619
      2980
      3212
    

    

      24
      GE
      7276
      9326
      9218
      7830
      8550
      8316
      6128
      5324
      4728
      ...
      6696
      6986
      8174
      7672
      6001
      6393
      6391
      6278
      6680
      6541
    

    

      25
      JU
      1252
      1823
      1447
      1466
      1728
      1521
      876
      658
      422
      ...
      786
      949
      1479
      1179
      810
      937
      994
      983
      1113
      1264
    

  



26 rows × 26 columns

As always, we decided to show the graphs of unemployment, this time, for both Swiss and foreigners.





In [27]:



    


if RESET:
    for index, row in ch_swiss_rate.iterrows():
        global id_canton_mapping
        row_values = row.iloc[2:].astype(float)
        plot = row_values.transpose().plot()
        row_values = ch_foreigner_rate.iloc[index][2:].astype(float)
        plot = row_values.transpose().plot()
        plt.ylim(0,10)
        plt.legend(['Swiss', 'Foreigners'])
        plt.xlabel('Year')
        plt.ylabel('% of jobseekers')
        plt.title('Graph of jobseekers in ' + id_canton_mapping[row.values[0]])
        plt.savefig('ch_graphs/graph_s_f_' + row.values[0] + '.png')
        plt.close()

In order to display the most informations in a single map, we decided to show small bar plots for each canton, showing the Swiss and the foreigners unemployment rates, as well as a color for each canton, indicating the difference.

First, we need to create these small bar plots, and save them. The produced graphs are then hosted on our personal server. As always, we only create them if RESET is set to true.





In [28]:



    


if RESET:
    for index, row in ch_swiss_rate.iterrows():
        row_values = [row.iloc[-1], ch_foreigner_rate.iloc[index][-1]]
        plot = plt.bar([0,1], row_values)
        plot[0].set_color('b')
        plot[1].set_color('orange')

        plt.ylim(0,6)
        plt.axis('off')
        plt.savefig('icon_ch/icon_' + row.values[0] + '.png')

        plt.close()

Now, we can write the function that outputs a color based on the difference between the two unemployment rates. We decided to use two linear scales, on for the negative values, and one for the positive ones. This is done to ensure that white values correspond to a small difference, and colorful ones for large difference either positive of negative.

Blue colors indicate that the unemployment rate is higher for swiss nationals while red colors indicate that the rate is higher for foregin nationals.





In [29]:



    


colors_s_f_neg = ['#b2182b','#d6604d','#f0b572','#fddbc7']
colors_s_f_pos = ['#d1e5f0','#92c5de','#4393c3','#2166ac']

diff_ch_for = ch_swiss_rate.drop('Canton', axis=1).subtract(ch_foreigner_rate.drop('Canton', axis=1))
diff_ch_for = pd.concat((ch_data['Canton'], diff_ch_for), axis=1)


min_s_f_ch = diff_ch_for['2017'].min()
max_s_f_ch = diff_ch_for['2017'].max()

def get_color_ch_s_f(canton, properties):
    diff_ch_s_f = diff_ch_for.loc[diff_ch_for['Canton'] == canton, '2017'].values
    if (diff_ch_s_f < 0):
        ratio = (diff_ch_s_f - min_s_f_ch) / (-min_s_f_ch)
        colors = colors_s_f_neg
    else:
        ratio = (diff_ch_s_f) / (max_s_f_ch)
        colors = colors_s_f_pos

        
    index = math.floor(ratio * len(colors))
    if index == len(colors):
        index = index - 1
    return colors[index]

As always, we can now create the map.





In [30]:



    


map_ch_s_f = folium.Map([46.8,8.3], tiles='cartodbpositron', zoom_start=8)


# Color of the cantons
folium.TopoJson(
    open('topojson/ch-cantons.topojson.json'),
    object_path='objects.cantons',
    style_function=lambda feature: {
        'fillOpacity' : 1,
        'opacity' : 1,
        'fillColor': get_color_ch_s_f(feature['id'], feature['properties']),
        'color' : 'black',
        'weight' : 1
        }
).add_to(map_ch_s_f)



    


















    
Out[30]:








<folium.features.TopoJson at 0x7f4d47d5b7f0>

And now we add the 'markers' (that are the small bar plots), that display the graphs.

You can see the map here :

https://nbviewer.jupyter.org/github/RadoslawDryzner/LeRepoDuGuerrier/blob/master/Homework03/map_foreigners_ch.ipynb





In [31]:



    


all_markers_s_f = folium.FeatureGroup("Markers")
for canton_id in id_canton_mapping:
    location = position_canton_mapping[canton_id]
    
    html = '<img src="http://feudal-ambitions.com/ADA/HW3/ch_graphs/graph_s_f_' + canton_id + '" width=450 height=300>'
    iframe = folium.IFrame(html, width=470, height=320)
    popup = folium.Popup(iframe, max_width=2650)

    custom_icon = folium.features.CustomIcon("http://feudal-ambitions.com/ADA/HW3/icon_ch/icon_"+ canton_id +".png", icon_size=(30, 60))
    all_markers_s_f.add_child(folium.Marker(location, popup=popup, icon=custom_icon))

map_ch_s_f.add_child(all_markers_s_f)
map_ch_s_f



    


















    
Out[31]:

We can see that there is some variability in each canton and while there's a bit more cantons with a higher swiss unemployment rate, there isn't a clear rule as to which canton has more unemployed people of either category. The biggest differences are seen in Valais and in Jura. This could be due to the fact that a lot of French people work in Switzerland's border cantons, leading to less job placements for Swiss nationals. On the other hand, Valais is pretty difficult to access from neighboring countries, meaning that swiss nationals can fill most of the placement opportunities in the canton.

For this second part, we used the amsat data containing the age information for January of 2017.

First we clean the data by removing unncecessary titles and columns like before. After that we split the data in 6: 2 dataframes for the nationality which are divided in 3 dataframe for each age category.

We then plot the data of all the dataframes grouped by canton.





In [32]:



    


ch_jobseeker_age = pd.read_csv('data/jobseeker_ch_for_age.csv', thousands="'")



    











In [33]:



    


# clean the data
ch_jobseeker_age = ch_jobseeker_age[ch_jobseeker_age['Classes dâge 15-24, 25-49, 50 ans et plus'] != 'Total']
ch_jobseeker_age = ch_jobseeker_age[ch_jobseeker_age['Nationalité'] != 'Total']
ch_jobseeker_age = ch_jobseeker_age[ch_jobseeker_age['Canton'] != 'Total']

# Split by Nationality
ch_jobseeker_age_foreigner = ch_jobseeker_age[ch_jobseeker_age['Nationalité'] == 'Etrangers']
ch_jobseeker_age_swiss = ch_jobseeker_age[ch_jobseeker_age['Nationalité'] == 'Suisses']



    











In [34]:



    


ch_jobseeker_age_foreigner_1 = (ch_jobseeker_age_foreigner[ch_jobseeker_age_foreigner['Classes dâge 15-24, 25-49, 50 ans et plus'] == '1'])
ch_jobseeker_age_foreigner_1 = ch_jobseeker_age_foreigner_1.drop(['Nationalité','Classes dâge 15-24, 25-49, 50 ans et plus', 'Unnamed: 3'], 1)

ch_jobseeker_age_foreigner_2 = (ch_jobseeker_age_foreigner[ch_jobseeker_age_foreigner['Classes dâge 15-24, 25-49, 50 ans et plus'] == '2'])
ch_jobseeker_age_foreigner_2 = ch_jobseeker_age_foreigner_2.drop(['Nationalité','Classes dâge 15-24, 25-49, 50 ans et plus', 'Unnamed: 3'], 1)

ch_jobseeker_age_foreigner_3 = (ch_jobseeker_age_foreigner[ch_jobseeker_age_foreigner['Classes dâge 15-24, 25-49, 50 ans et plus'] == '3'])
ch_jobseeker_age_foreigner_3 = ch_jobseeker_age_foreigner_3.drop(['Nationalité','Classes dâge 15-24, 25-49, 50 ans et plus', 'Unnamed: 3'], 1)



    











In [35]:



    


ch_jobseeker_age_swiss_1 = (ch_jobseeker_age_swiss[ch_jobseeker_age_swiss['Classes dâge 15-24, 25-49, 50 ans et plus'] == '1'])
ch_jobseeker_age_swiss_1 = ch_jobseeker_age_swiss_1.drop(['Nationalité','Classes dâge 15-24, 25-49, 50 ans et plus', 'Unnamed: 3'], 1)

ch_jobseeker_age_swiss_2 = (ch_jobseeker_age_swiss[ch_jobseeker_age_swiss['Classes dâge 15-24, 25-49, 50 ans et plus'] == '2'])
ch_jobseeker_age_swiss_2 = ch_jobseeker_age_swiss_2.drop(['Nationalité','Classes dâge 15-24, 25-49, 50 ans et plus', 'Unnamed: 3'], 1)

ch_jobseeker_age_swiss_3 = (ch_jobseeker_age_swiss[ch_jobseeker_age_swiss['Classes dâge 15-24, 25-49, 50 ans et plus'] == '3'])
ch_jobseeker_age_swiss_3 = ch_jobseeker_age_swiss_3.drop(['Nationalité','Classes dâge 15-24, 25-49, 50 ans et plus', 'Unnamed: 3'], 1)



    











In [36]:



    


width = 1/8 
colors_foreigners = ['#b2182b','#d6604d','#f0b572']
colors_swiss = ['#2166ac', '#4393c3', '#92c5de']
fig, ax = plt.subplots(figsize=(20,7))
pos = list(range(len(ch_jobseeker_age_foreigner_1['2017'])))
pop = active_pop['2017']

values = ch_jobseeker_age_foreigner_1['2017'].astype(float).values
plt.bar(pos, values/pop*100, width, color=colors_foreigners[0])

values = ch_jobseeker_age_foreigner_2['2017'].astype(float).values
plt.bar([p + width for p in pos], values/pop*100,  width, color=colors_foreigners[1])

values = ch_jobseeker_age_foreigner_3['2017'].astype(float).values
plt.bar([p + width*2 for p in pos], values/pop*100, width, color=colors_foreigners[2])

values = ch_jobseeker_age_swiss_1['2017'].astype(float).values
plt.bar([p + width*3.5 for p in pos], values/pop*100, width, color=colors_swiss[0])

values = ch_jobseeker_age_swiss_2['2017'].astype(float).values
plt.bar([p + width*4.5 for p in pos], values/pop*100, width, color=colors_swiss[1])

values = ch_jobseeker_age_swiss_3['2017'].astype(float).values
plt.bar([p + width*5.5 for p in pos], values/pop*100, width, color=colors_swiss[2])

ax.set_ylabel('Jobseekers (%)')

ax.set_title('Foreigner jobseekers')

ax.set_xticks([p + 3 * width for p in pos])

plt.legend(['Foreigner: 15-24', 'Foreigner: 25-49', 'Foreigner: 50 and more', 'Swiss: 15-24', 'Swiss: 25-49', 'Swiss: 50 and more',], loc='upper left')

catons_names = ch_jobseeker_age_foreigner_1['Canton'].values
ax.set_xticklabels(catons_names)
for tick in ax.get_xticklabels(): #We rotate the ticks for better readability
    tick.set_rotation(90)

plt.xlim(min(pos)-width, max(pos)+width*6)

plt.grid(axis='y')
plt.show()

We can see that there is a bigger difference between the age categories '25-49' and '50 and more' in the foreigners than in the Swiss. This could be due to the fact that foreigners that are over 50 years old tend to go back to their origin countries.





In [ ]:

Content source: RadoslawDryzner/LeRepoDuGuerrier

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