Data and Analysis.ipynb

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   "metadata": {},
   "source": [
    "Sundus Seif "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forced Displacement Population\n",
    "\n",
    "#### Data collecetd from UNHCR (The UN Refugee Agency): \n",
    "http://popstats.unhcr.org/en/persons_of_concern"
   ]
  },
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     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3063: DtypeWarning:\n",
      "\n",
      "Columns (3,4,5,8,9,10) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#raw data\n",
    "import pandas as pd\n",
    "\n",
    "df= pd.read_csv(r\"unhcr_popstats_export_persons_of_concern_all_data.csv\", encoding = \"ISO-8859-1\")"
   ]
  },
  {
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   "source": [
    "#libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import seaborn as sns\n"
   ]
  },
  {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Country / territory of asylum/residence</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Refugees (incl. refugee-like situations)</th>\n",
       "      <th>Asylum-seekers (pending cases)</th>\n",
       "      <th>Returned refugees</th>\n",
       "      <th>Internally displaced persons (IDPs)</th>\n",
       "      <th>Returned IDPs</th>\n",
       "      <th>Stateless persons</th>\n",
       "      <th>Others of concern</th>\n",
       "      <th>Total Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1951</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Various/Unknown</td>\n",
       "      <td>180000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1951</td>\n",
       "      <td>Austria</td>\n",
       "      <td>Various/Unknown</td>\n",
       "      <td>282000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>282000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1951</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Various/Unknown</td>\n",
       "      <td>55000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1951</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Various/Unknown</td>\n",
       "      <td>168511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1951</td>\n",
       "      <td>China, Hong Kong SAR</td>\n",
       "      <td>Various/Unknown</td>\n",
       "      <td>30000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30000</td>\n",
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       "    <tr>\n",
       "      <th>138783</th>\n",
       "      <td>2018</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>South Sudan</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
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       "      <td>Uganda</td>\n",
       "      <td>7</td>\n",
       "      <td>*</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>138785</th>\n",
       "      <td>2018</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>*</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>138786</th>\n",
       "      <td>2018</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Zimbabwe</td>\n",
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       "      <td>21</td>\n",
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       "      <td>110</td>\n",
       "      <td>131</td>\n",
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      "text/plain": [
       "        Year Country / territory of asylum/residence           Origin  \\\n",
       "0       1951                               Australia  Various/Unknown   \n",
       "1       1951                                 Austria  Various/Unknown   \n",
       "2       1951                                 Belgium  Various/Unknown   \n",
       "3       1951                                  Canada  Various/Unknown   \n",
       "4       1951                    China, Hong Kong SAR  Various/Unknown   \n",
       "...      ...                                     ...              ...   \n",
       "138783  2018                                Zimbabwe      South Sudan   \n",
       "138784  2018                                Zimbabwe           Uganda   \n",
       "138785  2018                                Zimbabwe     South Africa   \n",
       "138786  2018                                Zimbabwe           Zambia   \n",
       "138787  2018                                Zimbabwe         Zimbabwe   \n",
       "\n",
       "       Refugees (incl. refugee-like situations)  \\\n",
       "0                                        180000   \n",
       "1                                        282000   \n",
       "2                                         55000   \n",
       "3                                        168511   \n",
       "4                                         30000   \n",
       "...                                         ...   \n",
       "138783                                        8   \n",
       "138784                                        7   \n",
       "138785                                      NaN   \n",
       "138786                                      NaN   \n",
       "138787                                      NaN   \n",
       "\n",
       "       Asylum-seekers (pending cases) Returned refugees  \\\n",
       "0                                 NaN               NaN   \n",
       "1                                 NaN               NaN   \n",
       "2                                 NaN               NaN   \n",
       "3                                 NaN               NaN   \n",
       "4                                 NaN               NaN   \n",
       "...                               ...               ...   \n",
       "138783                            NaN               NaN   \n",
       "138784                              *               NaN   \n",
       "138785                              *               NaN   \n",
       "138786                            NaN               NaN   \n",
       "138787                            NaN                21   \n",
       "\n",
       "        Internally displaced persons (IDPs)  Returned IDPs Stateless persons  \\\n",
       "0                                       NaN            NaN               NaN   \n",
       "1                                       NaN            NaN               NaN   \n",
       "2                                       NaN            NaN               NaN   \n",
       "3                                       NaN            NaN               NaN   \n",
       "4                                       NaN            NaN               NaN   \n",
       "...                                     ...            ...               ...   \n",
       "138783                                  NaN            NaN               NaN   \n",
       "138784                                  NaN            NaN               NaN   \n",
       "138785                                  NaN            NaN               NaN   \n",
       "138786                                  NaN            NaN               NaN   \n",
       "138787                                  NaN            NaN               NaN   \n",
       "\n",
       "       Others of concern Total Population  \n",
       "0                    NaN           180000  \n",
       "1                    NaN           282000  \n",
       "2                    NaN            55000  \n",
       "3                    NaN           168511  \n",
       "4                    NaN            30000  \n",
       "...                  ...              ...  \n",
       "138783               NaN                8  \n",
       "138784               NaN               10  \n",
       "138785                 5                7  \n",
       "138786                 *                *  \n",
       "138787               110              131  \n",
       "\n",
       "[138788 rows x 11 columns]"
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    "## UNHCR Types of forcibly displaced persons\n",
    "\n",
    "#### Refugees\n",
    "A refugee is someone who fled his or her home and country owing to “a well-founded fear of persecution because of his/her race, religion, nationality, membership in a particular social group, or political opinion”, according to the United Nations 1951 Refugee Convention. Many refugees are in exile to escape the effects of natural or human-made disasters.\n",
    "\n",
    "\n",
    "#### Asylum Seekers\n",
    "\n",
    "Asylum seekers say they are refugees and have fled their homes as refugees do, but their claim to refugee status is not yet definitively evaluated in the country to which they fled.\n",
    "\n",
    "Someone who flees their home country due to war or any danger towards them or their family, enters another country and applies for asylum, i.e. the right to international protection, in another country.\n",
    "\n",
    "#### Internally displaced person (IDP)\n",
    "\n",
    "Internally Displaced Persons (IDPs) are people who have not crossed an international border but have moved to a different region than the one they call home within their own country.\n",
    "\n",
    "Someone who is forced to flee his or her home but who remains within his or her country's borders. They are often referred to as refugees, although they do not fall within the legal definitions of a refugee.\n",
    "\n",
    "#### Stateless person \n",
    "\n",
    "Stateless persons do not have a recognized nationality and do not belong to any country.\n",
    "\n",
    "Statelessness situations are usually caused by discrimination against certain groups. Their lack of identification — a citizenship certificate — can exclude them from access to important government services, including health care, education or employment.\n",
    "\n",
    "In international law is someone who is \"not considered as a national by any state under the operation of its law\". Some stateless people are also refugees. However, not all refugees are stateless, and many people who are stateless have never crossed an international border.\n",
    "\n",
    "\n",
    "#### Returnees\n",
    "\n",
    "Returnees are former refugees who return to their own countries or regions of origin after time in exile. Returnees need continuous support and reintegration assistance to ensure that they can rebuild their lives at home.\n"
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      "text/plain": [
       "        year asylum/residence_country   origin_country  refugees  \\\n",
       "0       1951                Australia  Various/Unknown  180000.0   \n",
       "1       1951                  Austria  Various/Unknown  282000.0   \n",
       "2       1951                  Belgium  Various/Unknown   55000.0   \n",
       "3       1951                   Canada  Various/Unknown  168511.0   \n",
       "4       1951     China, Hong Kong SAR  Various/Unknown   30000.0   \n",
       "...      ...                      ...              ...       ...   \n",
       "138783  2018                 Zimbabwe      South Sudan       8.0   \n",
       "138784  2018                 Zimbabwe           Uganda       7.0   \n",
       "138785  2018                 Zimbabwe     South Africa       0.0   \n",
       "138786  2018                 Zimbabwe           Zambia       0.0   \n",
       "138787  2018                 Zimbabwe         Zimbabwe       0.0   \n",
       "\n",
       "        asylum(pending-cases)  returned_refugees  \\\n",
       "0                         0.0                0.0   \n",
       "1                         0.0                0.0   \n",
       "2                         0.0                0.0   \n",
       "3                         0.0                0.0   \n",
       "4                         0.0                0.0   \n",
       "...                       ...                ...   \n",
       "138783                    0.0                0.0   \n",
       "138784                    0.0                0.0   \n",
       "138785                    0.0                0.0   \n",
       "138786                    0.0                0.0   \n",
       "138787                    0.0               21.0   \n",
       "\n",
       "        internally_displaced_persons(IDPs)  returned_IDPs  stateless_persons  \\\n",
       "0                                      0.0            0.0                0.0   \n",
       "1                                      0.0            0.0                0.0   \n",
       "2                                      0.0            0.0                0.0   \n",
       "3                                      0.0            0.0                0.0   \n",
       "4                                      0.0            0.0                0.0   \n",
       "...                                    ...            ...                ...   \n",
       "138783                                 0.0            0.0                0.0   \n",
       "138784                                 0.0            0.0                0.0   \n",
       "138785                                 0.0            0.0                0.0   \n",
       "138786                                 0.0            0.0                0.0   \n",
       "138787                                 0.0            0.0                0.0   \n",
       "\n",
       "        others_of_concern  total_population  \n",
       "0                     0.0          180000.0  \n",
       "1                     0.0          282000.0  \n",
       "2                     0.0           55000.0  \n",
       "3                     0.0          168511.0  \n",
       "4                     0.0           30000.0  \n",
       "...                   ...               ...  \n",
       "138783                0.0               8.0  \n",
       "138784                0.0              10.0  \n",
       "138785                5.0               7.0  \n",
       "138786                0.0               0.0  \n",
       "138787              110.0             131.0  \n",
       "\n",
       "[138788 rows x 11 columns]"
      ]
     },
     "execution_count": 565,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#clean data\n",
    "\n",
    "#replace * with nan\n",
    "df= df.replace('*',np.nan)\n",
    "#convert na to 0\n",
    "df=df.fillna(0)\n",
    "#convert to float\n",
    "#rename columns\n",
    "df=df.rename(columns={'Year':'year',\n",
    "                'Country / territory of asylum/residence':'asylum/residence_country',\n",
    "                   'Origin':'origin_country',\n",
    "                   'Refugees (incl. refugee-like situations)':'refugees',\n",
    "                   'Asylum-seekers (pending cases)':'asylum(pending-cases)',\n",
    "                   'Returned refugees':'returned_refugees',\n",
    "                   'Internally displaced persons (IDPs)':'internally_displaced_persons(IDPs)',\n",
    "                   'Returned IDPs':'returned_IDPs',\n",
    "                   'Stateless persons':'stateless_persons',\n",
    "                   'Others of concern':'others_of_concern',\n",
    "                   'Total Population':'total_population'\n",
    "    })\n",
    "\n",
    "df[['refugees','asylum(pending-cases)','returned_refugees','internally_displaced_persons(IDPs)','returned_IDPs','stateless_persons','others_of_concern','total_population']]=df[['refugees','asylum(pending-cases)','returned_refugees','internally_displaced_persons(IDPs)','returned_IDPs','stateless_persons','others_of_concern','total_population']].astype(float)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.to_datetime(df['year'],format='%Y', errors='ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forcibly displaced persons data per country as of 2018 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 566,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: FutureWarning:\n",
      "\n",
      "Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin_country</th>\n",
       "      <th>refugees</th>\n",
       "      <th>asylum(pending-cases)</th>\n",
       "      <th>returned_refugees</th>\n",
       "      <th>internally_displaced_persons(IDPs)</th>\n",
       "      <th>returned_IDPs</th>\n",
       "      <th>stateless_persons</th>\n",
       "      <th>others_of_concern</th>\n",
       "      <th>total_population</th>\n",
       "      <th>total_displaced</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2681232.0</td>\n",
       "      <td>310094.0</td>\n",
       "      <td>16220.0</td>\n",
       "      <td>2106893.0</td>\n",
       "      <td>73527.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>489859.0</td>\n",
       "      <td>5677872.0</td>\n",
       "      <td>5588078.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>13507.0</td>\n",
       "      <td>21066.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34582.0</td>\n",
       "      <td>34573.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>4156.0</td>\n",
       "      <td>7419.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11631.0</td>\n",
       "      <td>11586.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>8213.0</td>\n",
       "      <td>8894.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18020.0</td>\n",
       "      <td>35147.0</td>\n",
       "      <td>35127.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>334444.0</td>\n",
       "      <td>5560.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>340097.0</td>\n",
       "      <td>340072.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>116786.0</td>\n",
       "      <td>1799.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>118589.0</td>\n",
       "      <td>118585.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>31120.0</td>\n",
       "      <td>35844.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2144718.0</td>\n",
       "      <td>133600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2345338.0</td>\n",
       "      <td>2211682.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>256.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>509.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>15583.0</td>\n",
       "      <td>17107.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>32861.0</td>\n",
       "      <td>32800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>211 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     origin_country   refugees  asylum(pending-cases)  returned_refugees  \\\n",
       "0       Afghanistan  2681232.0               310094.0            16220.0   \n",
       "1           Albania    13507.0                21066.0                0.0   \n",
       "2           Algeria     4156.0                 7419.0                0.0   \n",
       "3           Andorra        0.0                    0.0                0.0   \n",
       "4            Angola     8213.0                 8894.0                0.0   \n",
       "..              ...        ...                    ...                ...   \n",
       "206        Viet Nam   334444.0                 5560.0                0.0   \n",
       "207  Western Sahara   116786.0                 1799.0                0.0   \n",
       "208           Yemen    31120.0                35844.0                0.0   \n",
       "209          Zambia      256.0                  253.0                0.0   \n",
       "210        Zimbabwe    15583.0                17107.0               21.0   \n",
       "\n",
       "     internally_displaced_persons(IDPs)  returned_IDPs  stateless_persons  \\\n",
       "0                             2106893.0        73527.0                0.0   \n",
       "1                                   0.0            0.0                0.0   \n",
       "2                                   0.0            0.0                0.0   \n",
       "3                                   0.0            0.0                0.0   \n",
       "4                                   0.0            0.0                0.0   \n",
       "..                                  ...            ...                ...   \n",
       "206                                 0.0            0.0                0.0   \n",
       "207                                 0.0            0.0                0.0   \n",
       "208                           2144718.0       133600.0                0.0   \n",
       "209                                 0.0            0.0                0.0   \n",
       "210                                 0.0            0.0                0.0   \n",
       "\n",
       "     others_of_concern  total_population  total_displaced  \n",
       "0             489859.0         5677872.0        5588078.0  \n",
       "1                  0.0           34582.0          34573.0  \n",
       "2                 11.0           11631.0          11586.0  \n",
       "3                  0.0               0.0              0.0  \n",
       "4              18020.0           35147.0          35127.0  \n",
       "..                 ...               ...              ...  \n",
       "206               68.0          340097.0         340072.0  \n",
       "207                0.0          118589.0         118585.0  \n",
       "208                0.0         2345338.0        2211682.0  \n",
       "209                0.0             517.0            509.0  \n",
       "210              110.0           32861.0          32800.0  \n",
       "\n",
       "[211 rows x 10 columns]"
      ]
     },
     "execution_count": 566,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the sum of total population displaced in 2018\n",
    "df_2018=df[df[\"year\"] == 2018]\n",
    "df_2018= df_2018.groupby(\"origin_country\")[\"refugees\",\"asylum(pending-cases)\",\"returned_refugees\",\"internally_displaced_persons(IDPs)\",\"returned_IDPs\",\"stateless_persons\",\"others_of_concern\",\"total_population\"].sum().reset_index()\n",
    "df_2018[\"total_displaced\"]=df_2018[\"refugees\"]+df_2018[\"asylum(pending-cases)\"]+df_2018[\"internally_displaced_persons(IDPs)\"]+df_2018[\"stateless_persons\"]+df_2018[\"others_of_concern\"]\n",
    "df_2018\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin_country</th>\n",
       "      <th>total_displaced</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Syrian Arab Rep.</td>\n",
       "      <td>12986246.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>8000699.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sudan_</td>\n",
       "      <td>6980490.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>5588078.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dem. Rep. of the Congo</td>\n",
       "      <td>5381400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Somalia</td>\n",
       "      <td>3653557.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Venezuela (Bolivarian Republic of)</td>\n",
       "      <td>3078183.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>2845440.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>2530735.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       origin_country  total_displaced\n",
       "0                    Syrian Arab Rep.       12986246.0\n",
       "1                            Colombia        8000699.0\n",
       "0                              Sudan_        6980490.0\n",
       "2                         Afghanistan        5588078.0\n",
       "3              Dem. Rep. of the Congo        5381400.0\n",
       "5                             Somalia        3653557.0\n",
       "6  Venezuela (Bolivarian Republic of)        3078183.0\n",
       "7                            Ethiopia        2845440.0\n",
       "9                             Nigeria        2530735.0"
      ]
     },
     "execution_count": 502,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### TEST AREA ###\n",
    "test=top_50[:10]\n",
    "add_sudan=pd.DataFrame({\"origin_country\":\"Sudan_\",\n",
    "                 \"total_displaced\": [6980490]\n",
    "                  })\n",
    "test =test.append(add_sudan, ignore_index=False)\n",
    "test = test.drop(4)\n",
    "test = test.drop(8)\n",
    "test.sort_values(\"total_displaced\", ascending=False)\n",
    "###South Sudan\t4322299.0\n",
    "###Sudan\t2658191.0\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### TEST AREA ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin_country</th>\n",
       "      <th>total_displaced</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Syrian Arab Rep.</td>\n",
       "      <td>12986246.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>8000699.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>5588078.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dem. Rep. of the Congo</td>\n",
       "      <td>5381400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>4186110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Somalia</td>\n",
       "      <td>3653557.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Venezuela (Bolivarian Republic of)</td>\n",
       "      <td>3078183.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>2845440.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>2656322.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>2530735.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Iraq</td>\n",
       "      <td>2439732.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>2211682.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>1625173.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>1545981.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Central African Rep.</td>\n",
       "      <td>1270774.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>732198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>640945.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Eritrea</td>\n",
       "      <td>585851.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>487221.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Various/Unknown</td>\n",
       "      <td>429336.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>340072.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Honduras</td>\n",
       "      <td>315760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>310318.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>China</td>\n",
       "      <td>306358.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>303918.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Mali</td>\n",
       "      <td>289379.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>276179.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>El Salvador</td>\n",
       "      <td>250584.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Serbia and Kosovo (S/RES/1244 (1999))</td>\n",
       "      <td>241188.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Iran (Islamic Rep. of)</td>\n",
       "      <td>217330.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>197913.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Chad</td>\n",
       "      <td>197279.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Libya</td>\n",
       "      <td>191088.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Niger</td>\n",
       "      <td>186599.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>164276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>163904.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Congo</td>\n",
       "      <td>163109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>161989.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>118585.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Palestinian</td>\n",
       "      <td>115591.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>111676.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Guatemala</td>\n",
       "      <td>106001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>104684.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>102616.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>79812.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>68471.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>63369.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>India</td>\n",
       "      <td>61549.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>CÌ«te d'Ivoire</td>\n",
       "      <td>61095.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Guinea</td>\n",
       "      <td>56486.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "                           origin_country  total_displaced\n",
       "0                        Syrian Arab Rep.       12986246.0\n",
       "1                                Colombia        8000699.0\n",
       "2                             Afghanistan        5588078.0\n",
       "3                  Dem. Rep. of the Congo        5381400.0\n",
       "4                             South Sudan        4186110.0\n",
       "5                                 Somalia        3653557.0\n",
       "6      Venezuela (Bolivarian Republic of)        3078183.0\n",
       "7                                Ethiopia        2845440.0\n",
       "8                                   Sudan        2656322.0\n",
       "9                                 Nigeria        2530735.0\n",
       "10                                   Iraq        2439732.0\n",
       "11                                  Yemen        2211682.0\n",
       "12                                Ukraine        1625173.0\n",
       "13                                Myanmar        1545981.0\n",
       "14                   Central African Rep.        1270774.0\n",
       "15                               Cameroon         732198.0\n",
       "16                             Azerbaijan         640945.0\n",
       "17                                Eritrea         585851.0\n",
       "18                                Burundi         487221.0\n",
       "19                        Various/Unknown         429336.0\n",
       "20                               Viet Nam         340072.0\n",
       "21                               Honduras         315760.0\n",
       "22                                Georgia         310318.0\n",
       "23                                  China         306358.0\n",
       "24                               Pakistan         303918.0\n",
       "25                                   Mali         289379.0\n",
       "26                                 Rwanda         276179.0\n",
       "27                            El Salvador         250584.0\n",
       "28  Serbia and Kosovo (S/RES/1244 (1999))         241188.0\n",
       "29                 Iran (Islamic Rep. of)         217330.0\n",
       "30                                 Uganda         197913.0\n",
       "31                                   Chad         197279.0\n",
       "32                                  Libya         191088.0\n",
       "33                                  Niger         186599.0\n",
       "34                 Bosnia and Herzegovina         164276.0\n",
       "35                              Sri Lanka         163904.0\n",
       "36                                  Congo         163109.0\n",
       "37                            Philippines         161989.0\n",
       "38                         Western Sahara         118585.0\n",
       "39                            Palestinian         115591.0\n",
       "40                                 Turkey         111676.0\n",
       "41                              Guatemala         106001.0\n",
       "42                     Russian Federation         104684.0\n",
       "43                                 Mexico         102616.0\n",
       "44                             Bangladesh          79812.0\n",
       "45                                  Haiti          68471.0\n",
       "46                           Burkina Faso          63369.0\n",
       "47                                  India          61549.0\n",
       "48                         CÌ«te d'Ivoire          61095.0\n",
       "49                                 Guinea          56486.0"
      ]
     },
     "execution_count": 540,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top 50 list as of 2018 \n",
    "#df2=df[df[\"year\"] == 2018]\n",
    "top_= df_2018.groupby(\"origin_country\")[\"total_displaced\"].sum().sort_values(ascending=False).reset_index()\n",
    "top_=top_.set_index(\"origin_country\").drop(\"Stateless\", axis=0).reset_index()\n",
    "\n",
    "top_[:50]\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
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      "                       origin_country  total_displaced\n",
      "0                    Syrian Arab Rep.       12986246.0\n",
      "1                            Colombia        8000699.0\n",
      "2                         Afghanistan        5588078.0\n",
      "3              Dem. Rep. of the Congo        5381400.0\n",
      "4                         South Sudan        4186110.0\n",
      "5                             Somalia        3653557.0\n",
      "6  Venezuela (Bolivarian Republic of)        3078183.0\n",
      "7                            Ethiopia        2845440.0\n",
      "8                               Sudan        2656322.0\n",
      "9                             Nigeria        2530735.0\n"
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   "source": [
    "#top 10 list as of 2018\n",
    "\n",
    "import plotly\n",
    "from plotly.graph_objs import Figure, Histogram, Layout\n",
    "from plotly.graph_objs import Bar\n",
    "\n",
    "\n",
    "top_10=top_[:10]\n",
    "print(top_10)\n",
    "\n",
    "data = [Bar(x=top_10['origin_country'],\n",
    "            y=top_10['total_displaced'],\n",
    "            text=top_10['origin_country'])]\n",
    "\n",
    "layout = Layout(title=\"Total Displaced Persons From Top 10 Countries\")\n",
    "\n",
    "fig = Figure(data=data, layout=layout)\n",
    "\n",
    "plotly.offline.iplot(fig,show_link=False)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "metadata": {},
   "outputs": [
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138783</th>\n",
       "      <td>2018</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>South Sudan</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138784</th>\n",
       "      <td>2018</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Uganda</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138785</th>\n",
       "      <td>2018</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138786</th>\n",
       "      <td>2018</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138787</th>\n",
       "      <td>2018</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>131.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>115717 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        year asylum/residence_country origin_country  refugees  \\\n",
       "23071   2000              Afghanistan    Afghanistan       0.0   \n",
       "23072   2000              Afghanistan          India       0.0   \n",
       "23073   2000              Afghanistan           Iraq       0.0   \n",
       "23074   2000              Afghanistan     Tajikistan       0.0   \n",
       "23075   2000              Afghanistan         Turkey       0.0   \n",
       "...      ...                      ...            ...       ...   \n",
       "138783  2018                 Zimbabwe    South Sudan       8.0   \n",
       "138784  2018                 Zimbabwe         Uganda       7.0   \n",
       "138785  2018                 Zimbabwe   South Africa       0.0   \n",
       "138786  2018                 Zimbabwe         Zambia       0.0   \n",
       "138787  2018                 Zimbabwe       Zimbabwe       0.0   \n",
       "\n",
       "        asylum(pending-cases)  returned_refugees  \\\n",
       "23071                     0.0                0.0   \n",
       "23072                     0.0               19.0   \n",
       "23073                     2.0                0.0   \n",
       "23074                     1.0                0.0   \n",
       "23075                     0.0                6.0   \n",
       "...                       ...                ...   \n",
       "138783                    0.0                0.0   \n",
       "138784                    0.0                0.0   \n",
       "138785                    0.0                0.0   \n",
       "138786                    0.0                0.0   \n",
       "138787                    0.0               21.0   \n",
       "\n",
       "        internally_displaced_persons(IDPs)  returned_IDPs  stateless_persons  \\\n",
       "23071                             758625.0            0.0                0.0   \n",
       "23072                                  0.0            0.0                0.0   \n",
       "23073                                  0.0            0.0                0.0   \n",
       "23074                                  0.0            0.0                0.0   \n",
       "23075                                  0.0            0.0                0.0   \n",
       "...                                    ...            ...                ...   \n",
       "138783                                 0.0            0.0                0.0   \n",
       "138784                                 0.0            0.0                0.0   \n",
       "138785                                 0.0            0.0                0.0   \n",
       "138786                                 0.0            0.0                0.0   \n",
       "138787                                 0.0            0.0                0.0   \n",
       "\n",
       "        others_of_concern  total_population  \n",
       "23071                 0.0          758625.0  \n",
       "23072                 0.0              19.0  \n",
       "23073                 0.0               2.0  \n",
       "23074                 0.0               1.0  \n",
       "23075                 0.0               6.0  \n",
       "...                   ...               ...  \n",
       "138783                0.0               8.0  \n",
       "138784                0.0              10.0  \n",
       "138785                5.0               7.0  \n",
       "138786                0.0               0.0  \n",
       "138787              110.0             131.0  \n",
       "\n",
       "[115717 rows x 11 columns]"
      ]
     },
     "execution_count": 533,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:18: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Population')"
      ]
     },
     "execution_count": 549,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize = (12,9))\n",
    "grid_size = (3,2)\n",
    "\n",
    "\n",
    "fig.text(x=0.8, y=0.95, s='',color='#524939')\n",
    "\n",
    "# Overlay multiple plots onto the same axis, which spans 1 entire column of the figure\n",
    "ax = plt.subplot2grid(grid_size, (0,0), colspan=2, rowspan=3)\n",
    "\n",
    "df3=df[df[\"year\"] >= 2000]\n",
    "\n",
    "df3[\"year\"]=df3[\"year\"].apply(int)\n",
    "\n",
    "#filter Syria \n",
    "syria=df3.loc[df3.origin_country=='Syrian Arab Rep.'].groupby(\"year\")[[\"total_population\"]].agg(\"sum\")\n",
    "syria=syria.rename(columns={'total_population':'Syria'})\n",
    "syria.plot(ax=ax,\n",
    "    legend=True, color=['r'], title=\"Displacement Population\")\n",
    "\n",
    "\n",
    "#filter Colombia \n",
    "colombia=df3.loc[df3.origin_country=='Colombia'].groupby(\"year\")[[\"total_population\"]].agg(\"sum\")\n",
    "colombia=colombia.rename(columns={'total_population':'Colombia'})\n",
    "colombia.plot(ax=ax, linestyle='dotted',\n",
    "    legend=True)\n",
    "\n",
    "\n",
    "#filter Afghanistan  \n",
    "afghanistan=df3.loc[df3.origin_country=='Afghanistan'].groupby(\"year\")[[\"total_population\"]].agg(\"sum\")\n",
    "afghanistan=afghanistan.rename(columns={'total_population':'Afghanistan'})\n",
    "afghanistan.plot(ax=ax, linestyle='dotted',\n",
    "    legend=True)\n",
    "\n",
    "\n",
    "#filter Congo\n",
    "congo =df3.loc[df3.origin_country=='Dem. Rep. of the Congo'].groupby(\"year\")[[\"total_population\"]].agg(\"sum\")\n",
    "congo=congo.rename(columns={'total_population':'Democratic Republic of the Congo'})\n",
    "congo.plot(ax=ax, linestyle='dotted',\n",
    "    legend=True)\n",
    "\n",
    "\n",
    "#filter South Sudan\n",
    "\n",
    "sudan=df3.loc[df3.origin_country=='South Sudan'].groupby(\"year\")[[\"total_population\"]].agg(\"sum\")\n",
    "sudan=sudan.rename(columns={'total_population':'South Sudan'})\n",
    "sudan.plot(ax=ax, linestyle='dotted',\n",
    "    legend=True)\n",
    "\n",
    "\n",
    "#filter Somalia\n",
    "somalia =df3.loc[df3.origin_country=='Somalia'].groupby(\"year\")[[\"total_population\"]].agg(\"sum\")\n",
    "somalia=somalia.rename(columns={'total_population':'Somalia'})\n",
    "somalia.plot(ax=ax, linestyle='dotted',\n",
    "    legend=True)\n",
    "\n",
    "\n",
    "#filter Sudan\n",
    "iraq =df3.loc[df3.origin_country=='Sudan'].groupby(\"year\")[[\"total_population\"]].agg(\"sum\")\n",
    "iraq=iraq.rename(columns={'total_population':'Sudan'})\n",
    "iraq.plot(ax=ax, linestyle='dotted',\n",
    "    legend=True)\n",
    "\n",
    "\n",
    "#filter Venezuela\n",
    "iraq =df3.loc[df3.origin_country=='Venezuela (Bolivarian Republic of)'].groupby(\"year\")[[\"total_population\"]].agg(\"sum\")\n",
    "iraq=iraq.rename(columns={'total_population':'Venezuela'})\n",
    "iraq.plot(ax=ax, linestyle='dotted',\n",
    "    legend=True)\n",
    "\n",
    "\n",
    "#filter Ethiopia\n",
    "ethiopia=df3.loc[df3.origin_country=='Ethiopia'].groupby(\"year\")[[\"total_population\"]].agg(\"sum\")\n",
    "ethiopia=ethiopia.rename(columns={'total_population':'Ethiopia'})\n",
    "ethiopia.plot(ax=ax, linestyle='dotted',\n",
    "    legend=True)\n",
    "\n",
    "\n",
    "#filter Nigeria\n",
    "nigeria=df3.loc[df3.origin_country=='Nigeria'].groupby(\"year\")[[\"total_population\"]].agg(\"sum\")\n",
    "nigeria=nigeria.rename(columns={'total_population':'Nigeria'})\n",
    "nigeria.plot(ax=ax, linestyle='dotted',\n",
    "    legend=True)\n",
    "\n",
    "plt.xticks(range(2000,2019))\n",
    "plt.ticklabel_format(useOffset=False, style='plain')\n",
    "plt.ylabel('Population')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observing Syrian Refugee Crisis \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 584,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>refugees</th>\n",
       "      <th>asylum(pending-cases)</th>\n",
       "      <th>returned_refugees</th>\n",
       "      <th>internally_displaced_persons(IDPs)</th>\n",
       "      <th>returned_IDPs</th>\n",
       "      <th>stateless_persons</th>\n",
       "      <th>others_of_concern</th>\n",
       "      <th>total_population</th>\n",
       "      <th>total_displaced</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>19931.0</td>\n",
       "      <td>14456.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14110.0</td>\n",
       "      <td>48497.0</td>\n",
       "      <td>48497.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>729022.0</td>\n",
       "      <td>26023.0</td>\n",
       "      <td>68573.0</td>\n",
       "      <td>2016500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6900.0</td>\n",
       "      <td>2847018.0</td>\n",
       "      <td>2778445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>2468332.0</td>\n",
       "      <td>41037.0</td>\n",
       "      <td>140761.0</td>\n",
       "      <td>6520800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9400.0</td>\n",
       "      <td>9180330.0</td>\n",
       "      <td>9039569.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>3887491.0</td>\n",
       "      <td>84542.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7632500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>11606526.0</td>\n",
       "      <td>11606526.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>4873243.0</td>\n",
       "      <td>245857.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>6563462.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8247.0</td>\n",
       "      <td>11690998.0</td>\n",
       "      <td>11690809.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>5524377.0</td>\n",
       "      <td>184218.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6325978.0</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8518.0</td>\n",
       "      <td>12643092.0</td>\n",
       "      <td>12043091.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>6308623.0</td>\n",
       "      <td>146721.0</td>\n",
       "      <td>77212.0</td>\n",
       "      <td>6150005.0</td>\n",
       "      <td>597152.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8596.0</td>\n",
       "      <td>13288334.0</td>\n",
       "      <td>12613945.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>6654339.0</td>\n",
       "      <td>139534.0</td>\n",
       "      <td>210947.0</td>\n",
       "      <td>6183920.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8453.0</td>\n",
       "      <td>13197248.0</td>\n",
       "      <td>12986246.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       refugees  asylum(pending-cases)  returned_refugees  \\\n",
       "year                                                        \n",
       "2011    19931.0                14456.0                0.0   \n",
       "2012   729022.0                26023.0            68573.0   \n",
       "2013  2468332.0                41037.0           140761.0   \n",
       "2014  3887491.0                84542.0                0.0   \n",
       "2015  4873243.0               245857.0              189.0   \n",
       "2016  5524377.0               184218.0                1.0   \n",
       "2017  6308623.0               146721.0            77212.0   \n",
       "2018  6654339.0               139534.0           210947.0   \n",
       "\n",
       "      internally_displaced_persons(IDPs)  returned_IDPs  stateless_persons  \\\n",
       "year                                                                         \n",
       "2011                                 0.0            0.0                0.0   \n",
       "2012                           2016500.0            0.0                0.0   \n",
       "2013                           6520800.0            0.0                0.0   \n",
       "2014                           7632500.0            0.0                0.0   \n",
       "2015                           6563462.0            0.0                0.0   \n",
       "2016                           6325978.0       600000.0                0.0   \n",
       "2017                           6150005.0       597152.0                0.0   \n",
       "2018                           6183920.0            0.0                0.0   \n",
       "\n",
       "      others_of_concern  total_population  total_displaced  \n",
       "year                                                        \n",
       "2011            14110.0           48497.0          48497.0  \n",
       "2012             6900.0         2847018.0        2778445.0  \n",
       "2013             9400.0         9180330.0        9039569.0  \n",
       "2014             1993.0        11606526.0       11606526.0  \n",
       "2015             8247.0        11690998.0       11690809.0  \n",
       "2016             8518.0        12643092.0       12043091.0  \n",
       "2017             8596.0        13288334.0       12613945.0  \n",
       "2018             8453.0        13197248.0       12986246.0  "
      ]
     },
     "execution_count": 584,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filter syria \n",
    "\n",
    "syria=df.loc[df.origin_country=='Syrian Arab Rep.']\n",
    "syria=syria[syria[\"year\"] >= 2000]\n",
    "syria[\"total_displaced\"]=syria[\"refugees\"]+syria[\"asylum(pending-cases)\"]+syria[\"internally_displaced_persons(IDPs)\"]+syria[\"stateless_persons\"]+syria[\"others_of_concern\"]\n",
    "\n",
    "group_year=syria.groupby(\"year\").agg(\"sum\")\n",
    "group_year[11:]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Increase since the start of the civil war 2011  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 602,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## seaborn.lineplot\n",
    "import seaborn as sns; sns.set()\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "line_=syria.groupby(\"year\")[\"total_displaced\"].sum().reset_index()[11:]\n",
    "ax = sns.lineplot(x=\"year\", y=\"total_displaced\", data= line_,color=\"coral\", label=\"syria\",)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 629,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "year=%{x}<br>total_displaced=%{y}<extra></extra>",
         "legendgroup": "",
         "line": {
          "color": "#636efa",
          "dash": "solid"
         },
         "mode": "lines",
         "name": "",
         "showlegend": false,
         "type": "scatter",
         "x": [
          2011,
          2012,
          2013,
          2014,
          2015,
          2016,
          2017,
          2018
         ],
         "xaxis": "x",
         "y": [
          48497,
          2778445,
          9039569,
          11606526,
          11690809,
          12043091,
          12613945,
          12986246
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "font": {
         "color": "black",
         "family": "sans-serif",
         "size": 12
        },
        "legend": {
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
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              "#7201a8"
             ],
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              "#9c179e"
             ],
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             ],
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             ],
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             ],
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             ],
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              "#fdca26"
             ],
             [
              1,
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             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
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             ],
             [
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             ],
             [
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             ],
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             ],
             [
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             [
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             ],
             [
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             ],
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             ],
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              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
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             ],
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             ],
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              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
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             ],
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             ],
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             ],
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             ],
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             ],
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             ],
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             ],
             [
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            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
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              "#0d0887"
             ],
             [
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             ],
             [
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             ],
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             ],
             [
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             ],
             [
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             ],
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             ],
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             ],
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             ],
             [
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            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#interactive line plot \n",
    "\n",
    "import plotly.express as px\n",
    "\n",
    "line_=syria.groupby(\"year\")[\"total_displaced\"].sum().reset_index()[11:]\n",
    "\n",
    "\n",
    "fig = px.line(line_, x=\"year\", y=\"total_displaced\", title='Rise of Syrian Displaced People')\n",
    "\n",
    "\n",
    "fig.update_layout(\n",
    "    xaxis_title=\"Year\",\n",
    "    yaxis_title=\"Total Displaced\",\n",
    "    font=dict(\n",
    "        family=\"sans-serif\",\n",
    "        size=12,\n",
    "        color=\"black\"\n",
    "    )\n",
    ")\n",
    "\n",
    "fig.show()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "metadata": {},
   "outputs": [
    {
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{\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Displaced population\"}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('b6302e64-8431-4bb7-ac1a-2b063394d433');\n",
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>refugees</th>\n",
       "      <th>asylum(pending-cases)</th>\n",
       "      <th>internally_displaced_persons(IDPs)</th>\n",
       "      <th>stateless_persons</th>\n",
       "      <th>others_of_concern</th>\n",
       "      <th>returned_refugees</th>\n",
       "      <th>returned_IDPs</th>\n",
       "      <th>returned_IDPs</th>\n",
       "      <th>total_population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>6654339.0</td>\n",
       "      <td>139534.0</td>\n",
       "      <td>6183920.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8453.0</td>\n",
       "      <td>210947.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13197248.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       refugees  asylum(pending-cases)  internally_displaced_persons(IDPs)  \\\n",
       "year                                                                         \n",
       "2018  6654339.0               139534.0                           6183920.0   \n",
       "\n",
       "      stateless_persons  others_of_concern  returned_refugees  returned_IDPs  \\\n",
       "year                                                                           \n",
       "2018                0.0             8453.0           210947.0            0.0   \n",
       "\n",
       "      returned_IDPs  total_population  \n",
       "year                                   \n",
       "2018            0.0        13197248.0  "
      ]
     },
     "execution_count": 548,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## pie graph comparing types displaced persons from syria\n",
    "\n",
    "\n",
    "from plotly.offline import iplot, init_notebook_mode\n",
    "init_notebook_mode(connected = True)\n",
    "import plotly.express as px\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "pie=syria[syria[\"year\"] == 2018].groupby(\"year\")[\"refugees\",\"asylum(pending-cases)\",\"internally_displaced_persons(IDPs)\",\"stateless_persons\",\"others_of_concern\", \"returned_refugees\",\"returned_IDPs\",\"returned_IDPs\",\"total_population\"].sum()\n",
    "\n",
    "displaced=[\"refugees\",\"asylum(pending-cases)\",\"internally_displaced_persons(IDPs)\",\"stateless_persons\",\"others_of_concern\", \"returned_refugees\",\"returned_IDPs\"]\n",
    "totals = [6654339.0,139534.0,6183920.0,0.0,8453.0,210947.0,0.0]\n",
    "percent=list(map(lambda x: x/13197248.0, totals))\n",
    "\n",
    "data1 = {\n",
    "   \"values\": totals,\n",
    "   \"labels\": displaced,\n",
    "   \"domain\": {\"column\": 0},\n",
    "   \"name\": \"displaced\",\n",
    "   \"hoverinfo\":\"label+percent+name\",\n",
    "   \"hole\": .4,\n",
    "   \"type\": \"pie\"\n",
    "}\n",
    "\n",
    "\n",
    "data = [data1]\n",
    "layout = go.Layout(\n",
    "   {\n",
    "      \"title\":\"Displaced population\",\n",
    "      \"grid\": {\"rows\": 1, \"columns\": 1},\n",
    "      \"annotations\": [\n",
    "         {\n",
    "            \"font\": {\n",
    "               \"size\": 20\n",
    "            },\n",
    "            \"showarrow\": False,\n",
    "            \"text\": \"Syria\",\n",
    "            \"x\": 0.5,\n",
    "            \"y\": 0.5\n",
    "         }\n",
    "      ]\n",
    "   }\n",
    ")\n",
    "fig = go.Figure(data = data, layout = layout)\n",
    "iplot(fig)\n",
    "\n",
    "\n",
    "\n",
    "pie\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Syrian refugees residing in different countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 580,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country / Territory of Asylum/Residence</th>\n",
       "      <th>Total Displaced</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>3622478.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lebanon</td>\n",
       "      <td>946219.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jordan</td>\n",
       "      <td>676283.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Germany</td>\n",
       "      <td>584461.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Iraq</td>\n",
       "      <td>252526.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>132871.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>111696.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>93502.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Austria</td>\n",
       "      <td>50717.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Greece</td>\n",
       "      <td>38035.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>33779.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Libya</td>\n",
       "      <td>23725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>19990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>France</td>\n",
       "      <td>19174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>18354.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>17376.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Spain</td>\n",
       "      <td>16085.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Cyprus</td>\n",
       "      <td>14813.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>14709.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Norway</td>\n",
       "      <td>14149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>11085.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>11055.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>10405.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>7725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>7499.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>6117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Italy</td>\n",
       "      <td>5849.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Canada</td>\n",
       "      <td>4361.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>3852.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Morocco</td>\n",
       "      <td>3506.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>2828.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Finland</td>\n",
       "      <td>2570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Romania</td>\n",
       "      <td>2442.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>2193.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>2166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Malta</td>\n",
       "      <td>1867.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Rep. of Korea</td>\n",
       "      <td>1236.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>1002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Hungary</td>\n",
       "      <td>976.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>946.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>927.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>890.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>846.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Australia</td>\n",
       "      <td>684.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>602.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Poland</td>\n",
       "      <td>551.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Czech Rep.</td>\n",
       "      <td>456.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>452.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Lithuania</td>\n",
       "      <td>445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>427.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country / Territory of Asylum/Residence  Total Displaced\n",
       "1                                   Turkey        3622478.0\n",
       "2                                  Lebanon         946219.0\n",
       "3                                   Jordan         676283.0\n",
       "4                                  Germany         584461.0\n",
       "5                                     Iraq         252526.0\n",
       "6                                    Egypt         132871.0\n",
       "7                                   Sweden         111696.0\n",
       "8                                    Sudan          93502.0\n",
       "9                                  Austria          50717.0\n",
       "10                                  Greece          38035.0\n",
       "11                             Netherlands          33779.0\n",
       "12                                   Libya          23725.0\n",
       "13                                 Denmark          19990.0\n",
       "14                                  France          19174.0\n",
       "15                             Switzerland          18354.0\n",
       "16                                Bulgaria          17376.0\n",
       "17                                   Spain          16085.0\n",
       "18                                  Cyprus          14813.0\n",
       "19                                 Armenia          14709.0\n",
       "20                                  Norway          14149.0\n",
       "21                                 Belgium          11085.0\n",
       "22                United States of America          11055.0\n",
       "23                          United Kingdom          10405.0\n",
       "24                                  Brazil           7725.0\n",
       "25                                 Algeria           7499.0\n",
       "26                    United Arab Emirates           6117.0\n",
       "27                                   Italy           5849.0\n",
       "28                                  Canada           4361.0\n",
       "29                                   Yemen           3852.0\n",
       "30                                 Morocco           3506.0\n",
       "31                                Malaysia           2828.0\n",
       "32                                 Finland           2570.0\n",
       "33                                 Romania           2442.0\n",
       "34                                 Ireland           2193.0\n",
       "35                            Saudi Arabia           2166.0\n",
       "36                                   Malta           1867.0\n",
       "37                           Rep. of Korea           1236.0\n",
       "38                      Russian Federation           1002.0\n",
       "39                                 Hungary            976.0\n",
       "40                                 Ukraine            946.0\n",
       "41                                 Tunisia            927.0\n",
       "42                               Argentina            890.0\n",
       "43                              Luxembourg            846.0\n",
       "44                               Australia            684.0\n",
       "45                                Portugal            602.0\n",
       "46                                  Poland            551.0\n",
       "47                              Czech Rep.            456.0\n",
       "48                                 Georgia            452.0\n",
       "49                               Lithuania            445.0\n",
       "50                                 Croatia            427.0"
      ]
     },
     "execution_count": 580,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "syria=df.loc[df.origin_country=='Syrian Arab Rep.']\n",
    "syria[\"total_displaced\"]=syria[\"refugees\"]+syria[\"asylum(pending-cases)\"]+syria[\"internally_displaced_persons(IDPs)\"]+syria[\"stateless_persons\"]+syria[\"others_of_concern\"]\n",
    "syria=syria[syria[\"year\"] == 2018]\n",
    "syria_asylum=syria.groupby(\"asylum/residence_country\")[\"total_displaced\"].sum().sort_values(ascending=False).reset_index()[1:51]\n",
    "syria_asylum.rename(columns={\"asylum/residence_country\": \"Country / Territory of Asylum/Residence\", \"total_displaced\": \"Total Displaced\"})\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 578,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from wordcloud import WordCloud\n",
    "wc = WordCloud(background_color=\"white\", max_words=2000, width=800, height=400)\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# generate word cloud syria_asylum for 20 countries\n",
    "wc.generate(' '.join(syria_asylum['asylum/residence_country'][0:20]))\n",
    "\n",
    "# show \n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.imshow(wc, interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}