Resettled refugees in Sweden
4 min read

Resettled refugees in Sweden

One of my friends is a Syrian refugee, who was granted asylum in Sweden last year. I also want to try data analysis, so it fits that I should analyze something that's relevant to my friend. This is my first ever analysis in pandas, apologies for code abomination in advance.

In this analysis, I use pandas for dataframe, numpy for dealing with numbers (because I need to count and do some math with it) and matplotlib for plotting graphs.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

The next step is to clean up the dataframe for further analysis. The steps are:

  • Read csv
  • Group by origin country and year resettled
  • Remove destination country column (because it's the same value)
  • Remove non-integer values (because you can't do math magic with it)
  • Convert year and value to integer (hello, math magic)
# data prep
df = pd.read_csv('unhcr_resettlement_residence_swe.csv')[1:]
df = df.groupby(['Origin', 'Year'], as_index=False).sum() # group by two columns
df = df.drop('Country / territory of asylum/residence', axis=1) # drop destination country column
df = df[(df != '*').all(1)] # remove any rows that has '*' value
df.Year = df.Year.astype(np.int64) # convert year to int
df.Value = df.Value.astype(np.int64) # convert value to int

df
Origin Year Value
0 Afghanistan 1984 7
1 Afghanistan 1985 4
2 Afghanistan 1986 4
3 Afghanistan 1987 1
4 Afghanistan 1988 1
5 Afghanistan 1991 2
6 Afghanistan 1992 18
7 Afghanistan 1997 1
8 Afghanistan 1998 5
9 Afghanistan 1999 16
10 Afghanistan 2000 339
11 Afghanistan 2001 270
12 Afghanistan 2002 156
13 Afghanistan 2003 244
14 Afghanistan 2004 314
15 Afghanistan 2005 183
16 Afghanistan 2006 353
17 Afghanistan 2007 185
18 Afghanistan 2008 414
19 Afghanistan 2009 318
20 Afghanistan 2010 336
21 Afghanistan 2011 404
22 Afghanistan 2012 438
23 Afghanistan 2013 219
24 Afghanistan 2014 328
25 Afghanistan 2015 222
26 Afghanistan 2016 20
27 Albania 1991 1
28 Albania 1992 1
29 Albania 2003 3
... ... ... ...
705 Various/unknown 2009 2
706 Various/unknown 2013 2
707 Venezuela (Bolivarian Republic of) 2015 4
708 Viet Nam 1984 76
709 Viet Nam 1985 48
710 Viet Nam 1986 171
711 Viet Nam 1987 232
712 Viet Nam 1988 94
713 Viet Nam 1990 939
714 Viet Nam 1991 656
715 Viet Nam 1992 474
716 Viet Nam 1993 197
717 Viet Nam 1994 32
718 Viet Nam 1995 4
719 Viet Nam 1997 21
720 Viet Nam 2002 1
721 Viet Nam 2004 10
722 Viet Nam 2006 10
723 Viet Nam 2009 2
724 Viet Nam 2010 6
726 Yemen 1992 1
727 Yemen 2004 1
728 Yemen 2005 4
729 Yemen 2006 1
730 Zimbabwe 2006 4
731 Zimbabwe 2008 1
732 Zimbabwe 2011 1
733 Zimbabwe 2014 7
734 Zimbabwe 2015 6
735 Zimbabwe 2016 9

725 rows × 3 columns

Since I want to plot a multiple line graph, I need to supply one dataframe per each line. This step is to create one dataframe per source country and clean it up. For example, if there is one year where no refugees are resettled, that year doesn't exist in the dataframe, so I have to check whether the years are missing or not, and if missing, create it and set the value to 0.

# create one dataframe per one origin country
UniqueNames = df.Origin.unique()
DataFrameDict = {elem : pd.DataFrame for elem in UniqueNames}

for key in DataFrameDict.keys():
    DataFrameDict[key] = df[:][df.Origin == key]

def clean_up_dataframe(df):
    country = df.Origin.unique()[0]
    df = df.drop('Origin', axis=1)
    df.index = df.Year
    df = df.drop('Year', axis=1)
    df = df.rename(columns={'Value': country})

    df2 = pd.DataFrame({'Year':range(1983,2016+1), country:0}) # dummy dataframe
    df2.index = df2.Year
    df2[country] = df[country]
    df2 = df2.fillna(0)
    df2 = df2[country]

    return df2

And because Syria is in the Middle East, I want to focus in the MENA region (Middle East and North Africa). However, the list is too big, and I've yet to figure out how to make it look pretty. What I do instead is group countries into each subregion and plot them.

# orginal MENA, too big
UniqueNames_og_mena = ['Algeria', 'Bahrain', 'Djibouti', 'Egypt', 'Iran', 'Iraq', 'Israel', 'Jordan',
'Kuwait', 'Lebanon', 'Libya', 'Mauritania', 'Morocco', 'Oman', 'Palestine', 'Qatar',
'Sahrawi Arab Democratic Republic', 'Saudi Arabia', 'Somalia', 'Sudan', 'Syria', 'Tunisia',
'United Arab Emirates', 'Yemen', 'Afghanistan', 'Armenia', 'Azerbaijan', 'Chad', 'Comoros',
'Cyprus', 'Eritrea', 'Georgia', 'Mali', 'Niger', 'Pakistan', 'Turkey']

# MENA
UniqueNames_mena = ['Algeria', 'Bahrain', 'Djibouti', 'Egypt', 'Iran (Islamic Rep. of)', 'Iraq', 'Jordan',
               'Kuwait', 'Lebanon', 'Libya', 'Mauritania', 'Saudi Arabia', 'Somalia', 'Sudan',
               'Syrian Arab Rep.', 'Tunisia', 'Yemen', 'Afghanistan',
               'Armenia', 'Azerbaijan', 'Chad', 'Eritrea', 'Georgia', 'Pakistan', 'Turkey']   

# LEVANT  
UniqueNames_levant = [ 'Iraq', 'Jordan', 'Lebanon', 'Syrian Arab Rep.']  

# NORTH AFRICA
UniqueNames_north_africa = ['Algeria', 'Djibouti', 'Egypt', 'Libya', 'Mauritania',  'Somalia', 'Sudan',
                'Tunisia', 'Chad', 'Eritrea']

def plot(region_name, region_list):
    df1 = clean_up_dataframe(DataFrameDict[region_list[0]])
    ax = df1.plot(figsize=(15,10))

    for i in region_list[1:]:
        df = clean_up_dataframe(DataFrameDict[i])
        df.plot(ax=ax)

    plt.xlabel('Year')
    plt.ylabel('Value')
    plt.title('Resettled Refugees in Sweden from {} Region Between 1983-2016'.format(region_name))
    ax.legend()

    plt.show()

# plot('All MENA', UniqueNames_og_mena) # list is too big
plot('MENA', UniqueNames_mena)

You can see that a lot of Iraqi refugees resettled between 1990-1995, which coincides with the Gulf War (1990-1).

plot('Levant', UniqueNames_levant)

This graph shows only refugees from the Levant region. As expected, a lot of Iraqis sought asylum during the 90's, but Syrian refugees spiked up after 2010, which coincides with Arab Spring (2010-2).

plot('North Africa', UniqueNames_north_africa)


In North Africa, Somalian refugees spiked up around 2010, which is the result from non-functioning government, which resulted in rising clan wars. Additionally, you can see that there are a lot of Eritrean refugees too, from indefinite conscription. Families of those who fled the military are also targeted.