Resettled refugees in Sweden

2018-07-30

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.