eda-notebook.ipynb

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    "### Exploratory Data Analysis and Visualization\n",
    "# Importing Modules\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import Normalize\n",
    "from matplotlib.cm import ScalarMappable\n",
    "import numpy as np\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9c91a31d-ff8b-40bf-80e5-6dba7942f5cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b59ab00f-9c13-426b-8b58-caad1e2aeb71",
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   "source": [
    "# Loading Country GeoData\n",
    "# Load the Natural Earth dataset\n",
    "world = gpd.read_file('./data/natural_earth_geo_admin0/ne_10m_admin_0_countries.shp')\n",
    "\n",
    "# Group ISO A3 codes by continent\n",
    "iso_a3_by_continent = world.groupby('CONTINENT')['ISO_A3'].apply(list).to_dict()\n",
    "\n",
    "# Iterate over the dictionary and remove '-99' values\n",
    "cleaned_iso = {continent: [country for country in countries if country != '-99'] for continent, countries in iso_a3_by_continent.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2bcaa41-90db-484b-9b7c-e0514afb73d6",
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   "source": [
    "## Loading of the datasets\n",
    "## Agriculture & Rural Development \n",
    "# Rural population (% of total population) For the 70 percent of the world's poor who live in rural areas, agriculture is the main source of income and employment. \n",
    "# But depletion and degradation of land and water pose serious challenges to producing enough food and other agricultural products to sustain livelihoods here and meet the needs of urban populations. \n",
    "# Data presented here include measures of agricultural inputs, outputs, and productivity compiled by the UN's Food and Agriculture Organization.\n",
    "# Dataset from WorldBank (26-May-2023)\n",
    "df = pd.read_csv(\"./data/worldbank_agri/API_1_DS2_en_csv_v2_5455649.csv\", skiprows=3)\n",
    "# https://api.worldbank.org/v2/en/topic/1?downloadformat=csv"
   ]
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   "execution_count": 5,
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    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Indicator Name</th>\n",
       "      <th>Indicator Code</th>\n",
       "      <th>1960</th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>1964</th>\n",
       "      <th>1965</th>\n",
       "      <th>...</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>Agricultural raw materials exports (% of merch...</td>\n",
       "      <td>TX.VAL.AGRI.ZS.UN</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>...</td>\n",
       "      <td>0.046919</td>\n",
       "      <td>0.055358</td>\n",
       "      <td>0.092621</td>\n",
       "      <td>0.085188</td>\n",
       "      <td>0.079480</td>\n",
       "      <td>0.076267</td>\n",
       "      <td>0.104098</td>\n",
       "      <td>0.069199</td>\n",
       "      <td>0.102736</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>Agricultural raw materials imports (% of merch...</td>\n",
       "      <td>TM.VAL.AGRI.ZS.UN</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>...</td>\n",
       "      <td>0.303687</td>\n",
       "      <td>0.398228</td>\n",
       "      <td>0.496427</td>\n",
       "      <td>0.335550</td>\n",
       "      <td>0.297792</td>\n",
       "      <td>0.311423</td>\n",
       "      <td>0.350670</td>\n",
       "      <td>0.339582</td>\n",
       "      <td>0.353604</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>Rural population (% of total population)</td>\n",
       "      <td>SP.RUR.TOTL.ZS</td>\n",
       "      <td>49.224</td>\n",
       "      <td>49.239000</td>\n",
       "      <td>49.254000</td>\n",
       "      <td>49.2700</td>\n",
       "      <td>49.285000</td>\n",
       "      <td>49.300000</td>\n",
       "      <td>...</td>\n",
       "      <td>57.010000</td>\n",
       "      <td>56.959000</td>\n",
       "      <td>56.892000</td>\n",
       "      <td>56.808000</td>\n",
       "      <td>56.707000</td>\n",
       "      <td>56.589000</td>\n",
       "      <td>56.454000</td>\n",
       "      <td>56.303000</td>\n",
       "      <td>56.134000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>Rural population growth (annual %)</td>\n",
       "      <td>SP.RUR.TOTL.ZG</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.211234</td>\n",
       "      <td>1.577679</td>\n",
       "      <td>1.4226</td>\n",
       "      <td>1.245827</td>\n",
       "      <td>1.065002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.691192</td>\n",
       "      <td>0.601746</td>\n",
       "      <td>0.520623</td>\n",
       "      <td>0.442423</td>\n",
       "      <td>0.358555</td>\n",
       "      <td>0.287256</td>\n",
       "      <td>0.213237</td>\n",
       "      <td>-0.133220</td>\n",
       "      <td>-0.347205</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>Rural population</td>\n",
       "      <td>SP.RUR.TOTL</td>\n",
       "      <td>26880.000</td>\n",
       "      <td>27481.000000</td>\n",
       "      <td>27918.000000</td>\n",
       "      <td>28318.0000</td>\n",
       "      <td>28673.000000</td>\n",
       "      <td>28980.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>58652.000000</td>\n",
       "      <td>59006.000000</td>\n",
       "      <td>59314.000000</td>\n",
       "      <td>59577.000000</td>\n",
       "      <td>59791.000000</td>\n",
       "      <td>59963.000000</td>\n",
       "      <td>60091.000000</td>\n",
       "      <td>60011.000000</td>\n",
       "      <td>59803.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 67 columns</p>\n",
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      "text/plain": [
       "  Country Name Country Code  \\\n",
       "0        Aruba          ABW   \n",
       "1        Aruba          ABW   \n",
       "2        Aruba          ABW   \n",
       "3        Aruba          ABW   \n",
       "4        Aruba          ABW   \n",
       "\n",
       "                                      Indicator Name     Indicator Code  \\\n",
       "0  Agricultural raw materials exports (% of merch...  TX.VAL.AGRI.ZS.UN   \n",
       "1  Agricultural raw materials imports (% of merch...  TM.VAL.AGRI.ZS.UN   \n",
       "2           Rural population (% of total population)     SP.RUR.TOTL.ZS   \n",
       "3                 Rural population growth (annual %)     SP.RUR.TOTL.ZG   \n",
       "4                                   Rural population        SP.RUR.TOTL   \n",
       "\n",
       "        1960          1961          1962        1963          1964  \\\n",
       "0        NaN           NaN           NaN         NaN           NaN   \n",
       "1        NaN           NaN           NaN         NaN           NaN   \n",
       "2     49.224     49.239000     49.254000     49.2700     49.285000   \n",
       "3        NaN      2.211234      1.577679      1.4226      1.245827   \n",
       "4  26880.000  27481.000000  27918.000000  28318.0000  28673.000000   \n",
       "\n",
       "           1965  ...          2013          2014          2015          2016  \\\n",
       "0           NaN  ...      0.046919      0.055358      0.092621      0.085188   \n",
       "1           NaN  ...      0.303687      0.398228      0.496427      0.335550   \n",
       "2     49.300000  ...     57.010000     56.959000     56.892000     56.808000   \n",
       "3      1.065002  ...      0.691192      0.601746      0.520623      0.442423   \n",
       "4  28980.000000  ...  58652.000000  59006.000000  59314.000000  59577.000000   \n",
       "\n",
       "           2017          2018          2019          2020          2021  2022  \n",
       "0      0.079480      0.076267      0.104098      0.069199      0.102736   NaN  \n",
       "1      0.297792      0.311423      0.350670      0.339582      0.353604   NaN  \n",
       "2     56.707000     56.589000     56.454000     56.303000     56.134000   NaN  \n",
       "3      0.358555      0.287256      0.213237     -0.133220     -0.347205   NaN  \n",
       "4  59791.000000  59963.000000  60091.000000  60011.000000  59803.000000   NaN  \n",
       "\n",
       "[5 rows x 67 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Databank dataset inspection\n",
    "df.drop(columns=[\"Unnamed: 67\"], inplace=True) #Obsolete column with Null values\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ea669c04-c073-4e58-a37e-7885ef01282e",
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       "(11172, 67)"
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   ],
   "source": [
    "# Dimensions of DataFrame\n",
    "df.shape"
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
   "id": "2c78f3d9-50d6-4ce7-89e2-c6f97ca84c79",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11172 entries, 0 to 11171\n",
      "Data columns (total 67 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Country Name    11172 non-null  object \n",
      " 1   Country Code    11172 non-null  object \n",
      " 2   Indicator Name  11172 non-null  object \n",
      " 3   Indicator Code  11172 non-null  object \n",
      " 4   1960            632 non-null    float64\n",
      " 5   1961            4555 non-null   float64\n",
      " 6   1962            4748 non-null   float64\n",
      " 7   1963            4765 non-null   float64\n",
      " 8   1964            4775 non-null   float64\n",
      " 9   1965            4826 non-null   float64\n",
      " 10  1966            4858 non-null   float64\n",
      " 11  1967            4871 non-null   float64\n",
      " 12  1968            4889 non-null   float64\n",
      " 13  1969            4885 non-null   float64\n",
      " 14  1970            5364 non-null   float64\n",
      " 15  1971            5368 non-null   float64\n",
      " 16  1972            5374 non-null   float64\n",
      " 17  1973            5376 non-null   float64\n",
      " 18  1974            5404 non-null   float64\n",
      " 19  1975            5431 non-null   float64\n",
      " 20  1976            5434 non-null   float64\n",
      " 21  1977            5441 non-null   float64\n",
      " 22  1978            5439 non-null   float64\n",
      " 23  1979            5433 non-null   float64\n",
      " 24  1980            5435 non-null   float64\n",
      " 25  1981            5472 non-null   float64\n",
      " 26  1982            5448 non-null   float64\n",
      " 27  1983            5462 non-null   float64\n",
      " 28  1984            5457 non-null   float64\n",
      " 29  1985            5454 non-null   float64\n",
      " 30  1986            5449 non-null   float64\n",
      " 31  1987            5482 non-null   float64\n",
      " 32  1988            5309 non-null   float64\n",
      " 33  1989            5357 non-null   float64\n",
      " 34  1990            7577 non-null   float64\n",
      " 35  1991            7319 non-null   float64\n",
      " 36  1992            7754 non-null   float64\n",
      " 37  1993            7873 non-null   float64\n",
      " 38  1994            7918 non-null   float64\n",
      " 39  1995            8028 non-null   float64\n",
      " 40  1996            8072 non-null   float64\n",
      " 41  1997            8093 non-null   float64\n",
      " 42  1998            8085 non-null   float64\n",
      " 43  1999            8097 non-null   float64\n",
      " 44  2000            9295 non-null   float64\n",
      " 45  2001            8214 non-null   float64\n",
      " 46  2002            8261 non-null   float64\n",
      " 47  2003            8251 non-null   float64\n",
      " 48  2004            8237 non-null   float64\n",
      " 49  2005            8231 non-null   float64\n",
      " 50  2006            8230 non-null   float64\n",
      " 51  2007            8245 non-null   float64\n",
      " 52  2008            8177 non-null   float64\n",
      " 53  2009            7647 non-null   float64\n",
      " 54  2010            7651 non-null   float64\n",
      " 55  2011            7636 non-null   float64\n",
      " 56  2012            7668 non-null   float64\n",
      " 57  2013            7696 non-null   float64\n",
      " 58  2014            7716 non-null   float64\n",
      " 59  2015            8776 non-null   float64\n",
      " 60  2016            7736 non-null   float64\n",
      " 61  2017            7728 non-null   float64\n",
      " 62  2018            7711 non-null   float64\n",
      " 63  2019            7684 non-null   float64\n",
      " 64  2020            7182 non-null   float64\n",
      " 65  2021            3540 non-null   float64\n",
      " 66  2022            170 non-null    float64\n",
      "dtypes: float64(63), object(4)\n",
      "memory usage: 5.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# Dataframe Info\n",
    "df.info()"
   ]
  },
  {
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   "execution_count": 8,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1960</th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>1964</th>\n",
       "      <th>1965</th>\n",
       "      <th>1966</th>\n",
       "      <th>1967</th>\n",
       "      <th>1968</th>\n",
       "      <th>1969</th>\n",
       "      <th>...</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.320000e+02</td>\n",
       "      <td>4.555000e+03</td>\n",
       "      <td>4.748000e+03</td>\n",
       "      <td>4.765000e+03</td>\n",
       "      <td>4.775000e+03</td>\n",
       "      <td>4.826000e+03</td>\n",
       "      <td>4.858000e+03</td>\n",
       "      <td>4.871000e+03</td>\n",
       "      <td>4.889000e+03</td>\n",
       "      <td>4.885000e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>7.696000e+03</td>\n",
       "      <td>7.716000e+03</td>\n",
       "      <td>8.776000e+03</td>\n",
       "      <td>7.736000e+03</td>\n",
       "      <td>7.728000e+03</td>\n",
       "      <td>7.711000e+03</td>\n",
       "      <td>7.684000e+03</td>\n",
       "      <td>7.182000e+03</td>\n",
       "      <td>3.540000e+03</td>\n",
       "      <td>170.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.093038e+09</td>\n",
       "      <td>1.654885e+08</td>\n",
       "      <td>1.628815e+08</td>\n",
       "      <td>1.797908e+08</td>\n",
       "      <td>1.983275e+08</td>\n",
       "      <td>2.393604e+08</td>\n",
       "      <td>2.381413e+08</td>\n",
       "      <td>2.499119e+08</td>\n",
       "      <td>2.580527e+08</td>\n",
       "      <td>3.306279e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>4.349823e+09</td>\n",
       "      <td>4.467862e+09</td>\n",
       "      <td>3.778675e+09</td>\n",
       "      <td>4.305758e+09</td>\n",
       "      <td>4.535517e+09</td>\n",
       "      <td>4.600358e+09</td>\n",
       "      <td>4.761723e+09</td>\n",
       "      <td>5.373643e+09</td>\n",
       "      <td>1.217253e+10</td>\n",
       "      <td>1.532650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.458841e+09</td>\n",
       "      <td>3.343148e+09</td>\n",
       "      <td>3.362091e+09</td>\n",
       "      <td>3.735752e+09</td>\n",
       "      <td>4.145210e+09</td>\n",
       "      <td>4.588822e+09</td>\n",
       "      <td>4.568374e+09</td>\n",
       "      <td>4.808793e+09</td>\n",
       "      <td>4.953196e+09</td>\n",
       "      <td>6.608612e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>7.830856e+10</td>\n",
       "      <td>8.069766e+10</td>\n",
       "      <td>7.349175e+10</td>\n",
       "      <td>7.880005e+10</td>\n",
       "      <td>8.280874e+10</td>\n",
       "      <td>8.440930e+10</td>\n",
       "      <td>8.766616e+10</td>\n",
       "      <td>9.616307e+10</td>\n",
       "      <td>1.536880e+11</td>\n",
       "      <td>1.548552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-2.498824e+00</td>\n",
       "      <td>-3.171646e+00</td>\n",
       "      <td>-3.206682e+00</td>\n",
       "      <td>-3.078607e+00</td>\n",
       "      <td>-3.303554e+00</td>\n",
       "      <td>-3.563265e+00</td>\n",
       "      <td>-3.820587e+00</td>\n",
       "      <td>-1.051712e+01</td>\n",
       "      <td>-1.300982e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.096810e+00</td>\n",
       "      <td>-5.007410e+00</td>\n",
       "      <td>-5.802638e+00</td>\n",
       "      <td>-4.673748e+00</td>\n",
       "      <td>-4.702139e+00</td>\n",
       "      <td>-5.145168e+00</td>\n",
       "      <td>-5.989875e+00</td>\n",
       "      <td>-7.646281e+00</td>\n",
       "      <td>-8.434153e+00</td>\n",
       "      <td>0.024394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.111000e+01</td>\n",
       "      <td>1.427678e+01</td>\n",
       "      <td>1.162989e+01</td>\n",
       "      <td>1.170793e+01</td>\n",
       "      <td>1.171388e+01</td>\n",
       "      <td>1.197647e+01</td>\n",
       "      <td>1.186685e+01</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>1.210323e+01</td>\n",
       "      <td>1.243000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>9.454018e+00</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>5.917213e+00</td>\n",
       "      <td>9.960394e+00</td>\n",
       "      <td>9.984725e+00</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>1.017359e+01</td>\n",
       "      <td>8.719629e+00</td>\n",
       "      <td>5.023440e+00</td>\n",
       "      <td>0.717613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.584850e+01</td>\n",
       "      <td>9.538300e+01</td>\n",
       "      <td>8.596012e+01</td>\n",
       "      <td>8.657000e+01</td>\n",
       "      <td>8.719200e+01</td>\n",
       "      <td>8.648092e+01</td>\n",
       "      <td>8.715750e+01</td>\n",
       "      <td>8.810900e+01</td>\n",
       "      <td>8.808200e+01</td>\n",
       "      <td>8.788000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>8.878239e+01</td>\n",
       "      <td>9.047000e+01</td>\n",
       "      <td>8.605286e+01</td>\n",
       "      <td>9.227262e+01</td>\n",
       "      <td>9.128500e+01</td>\n",
       "      <td>9.019043e+01</td>\n",
       "      <td>9.186401e+01</td>\n",
       "      <td>7.899754e+01</td>\n",
       "      <td>6.889085e+01</td>\n",
       "      <td>1.098018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.539689e+06</td>\n",
       "      <td>1.051960e+05</td>\n",
       "      <td>8.646250e+04</td>\n",
       "      <td>8.470000e+04</td>\n",
       "      <td>8.736000e+04</td>\n",
       "      <td>8.981500e+04</td>\n",
       "      <td>8.998100e+04</td>\n",
       "      <td>9.022000e+04</td>\n",
       "      <td>9.117000e+04</td>\n",
       "      <td>9.391000e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>1.900103e+04</td>\n",
       "      <td>1.867001e+04</td>\n",
       "      <td>1.841500e+04</td>\n",
       "      <td>1.841449e+04</td>\n",
       "      <td>1.880100e+04</td>\n",
       "      <td>1.925345e+04</td>\n",
       "      <td>1.967540e+04</td>\n",
       "      <td>1.487000e+04</td>\n",
       "      <td>7.134665e+03</td>\n",
       "      <td>1.801351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.040337e+11</td>\n",
       "      <td>1.079593e+11</td>\n",
       "      <td>1.109413e+11</td>\n",
       "      <td>1.231941e+11</td>\n",
       "      <td>1.366833e+11</td>\n",
       "      <td>1.475283e+11</td>\n",
       "      <td>1.460780e+11</td>\n",
       "      <td>1.547463e+11</td>\n",
       "      <td>1.597173e+11</td>\n",
       "      <td>2.817427e+11</td>\n",
       "      <td>...</td>\n",
       "      <td>3.226817e+12</td>\n",
       "      <td>3.302406e+12</td>\n",
       "      <td>3.177582e+12</td>\n",
       "      <td>3.197479e+12</td>\n",
       "      <td>3.376945e+12</td>\n",
       "      <td>3.419240e+12</td>\n",
       "      <td>3.513631e+12</td>\n",
       "      <td>3.704093e+12</td>\n",
       "      <td>4.154467e+12</td>\n",
       "      <td>10.796677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               1960          1961          1962          1963          1964  \\\n",
       "count  6.320000e+02  4.555000e+03  4.748000e+03  4.765000e+03  4.775000e+03   \n",
       "mean   1.093038e+09  1.654885e+08  1.628815e+08  1.797908e+08  1.983275e+08   \n",
       "std    8.458841e+09  3.343148e+09  3.362091e+09  3.735752e+09  4.145210e+09   \n",
       "min    0.000000e+00 -2.498824e+00 -3.171646e+00 -3.206682e+00 -3.078607e+00   \n",
       "25%    6.111000e+01  1.427678e+01  1.162989e+01  1.170793e+01  1.171388e+01   \n",
       "50%    9.584850e+01  9.538300e+01  8.596012e+01  8.657000e+01  8.719200e+01   \n",
       "75%    4.539689e+06  1.051960e+05  8.646250e+04  8.470000e+04  8.736000e+04   \n",
       "max    1.040337e+11  1.079593e+11  1.109413e+11  1.231941e+11  1.366833e+11   \n",
       "\n",
       "               1965          1966          1967          1968          1969  \\\n",
       "count  4.826000e+03  4.858000e+03  4.871000e+03  4.889000e+03  4.885000e+03   \n",
       "mean   2.393604e+08  2.381413e+08  2.499119e+08  2.580527e+08  3.306279e+08   \n",
       "std    4.588822e+09  4.568374e+09  4.808793e+09  4.953196e+09  6.608612e+09   \n",
       "min   -3.303554e+00 -3.563265e+00 -3.820587e+00 -1.051712e+01 -1.300982e+01   \n",
       "25%    1.197647e+01  1.186685e+01  1.200000e+01  1.210323e+01  1.243000e+01   \n",
       "50%    8.648092e+01  8.715750e+01  8.810900e+01  8.808200e+01  8.788000e+01   \n",
       "75%    8.981500e+04  8.998100e+04  9.022000e+04  9.117000e+04  9.391000e+04   \n",
       "max    1.475283e+11  1.460780e+11  1.547463e+11  1.597173e+11  2.817427e+11   \n",
       "\n",
       "       ...          2013          2014          2015          2016  \\\n",
       "count  ...  7.696000e+03  7.716000e+03  8.776000e+03  7.736000e+03   \n",
       "mean   ...  4.349823e+09  4.467862e+09  3.778675e+09  4.305758e+09   \n",
       "std    ...  7.830856e+10  8.069766e+10  7.349175e+10  7.880005e+10   \n",
       "min    ... -5.096810e+00 -5.007410e+00 -5.802638e+00 -4.673748e+00   \n",
       "25%    ...  9.454018e+00  1.000000e+01  5.917213e+00  9.960394e+00   \n",
       "50%    ...  8.878239e+01  9.047000e+01  8.605286e+01  9.227262e+01   \n",
       "75%    ...  1.900103e+04  1.867001e+04  1.841500e+04  1.841449e+04   \n",
       "max    ...  3.226817e+12  3.302406e+12  3.177582e+12  3.197479e+12   \n",
       "\n",
       "               2017          2018          2019          2020          2021  \\\n",
       "count  7.728000e+03  7.711000e+03  7.684000e+03  7.182000e+03  3.540000e+03   \n",
       "mean   4.535517e+09  4.600358e+09  4.761723e+09  5.373643e+09  1.217253e+10   \n",
       "std    8.280874e+10  8.440930e+10  8.766616e+10  9.616307e+10  1.536880e+11   \n",
       "min   -4.702139e+00 -5.145168e+00 -5.989875e+00 -7.646281e+00 -8.434153e+00   \n",
       "25%    9.984725e+00  1.000000e+01  1.017359e+01  8.719629e+00  5.023440e+00   \n",
       "50%    9.128500e+01  9.019043e+01  9.186401e+01  7.899754e+01  6.889085e+01   \n",
       "75%    1.880100e+04  1.925345e+04  1.967540e+04  1.487000e+04  7.134665e+03   \n",
       "max    3.376945e+12  3.419240e+12  3.513631e+12  3.704093e+12  4.154467e+12   \n",
       "\n",
       "             2022  \n",
       "count  170.000000  \n",
       "mean     1.532650  \n",
       "std      1.548552  \n",
       "min      0.024394  \n",
       "25%      0.717613  \n",
       "50%      1.098018  \n",
       "75%      1.801351  \n",
       "max     10.796677  \n",
       "\n",
       "[8 rows x 63 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe DataFrame - Note this is the summary of all indicators, after preprocessing we will look at better overview.\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c1317472-3ddf-4d3e-b905-060011bc9fdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a reusable function for plotting missing values\n",
    "def missing_plot(\n",
    "        df_missing,\n",
    "        len_original,\n",
    "        title='',\n",
    "        xlabel='Columns',\n",
    "        ylabel='% of Missing Values',\n",
    "        cbar_label='% Missing',\n",
    "        cm=plt.cm.coolwarm,\n",
    "        hline=False,\n",
    "        hline_y=0,\n",
    "        hline_label='Threshold at',\n",
    "        hline_color='red'\n",
    "    ):\n",
    "    # Normalize the missing values to map them to colors\n",
    "    normalized_values = (df_missing - df_missing.min()) / (df_missing.max() - df_missing.min())\n",
    "    \n",
    "    # Create a colormap (you can choose any colormap you like)\n",
    "    cmap = cm\n",
    "    \n",
    "    # Create a figure and axes\n",
    "    fig, ax = plt.subplots(figsize=(24, 6))\n",
    "    \n",
    "    # Create a bar plot with custom colors\n",
    "    bars = sns.barplot(ax=ax, x=df_missing.index, y=df_missing.values / len_original, palette=cmap(normalized_values))\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.xticks(rotation=90)\n",
    "    \n",
    "    # Create a color bar legend for reference\n",
    "    sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=df_missing.min()/len_original, vmax=df_missing.max()/len_original))\n",
    "    sm.set_array([])\n",
    "    \n",
    "    # Add the colorbar to the same axis (ax) where the bars are plotted\n",
    "    cbar = plt.colorbar(sm, ax=ax)\n",
    "    cbar.set_label(cbar_label)\n",
    "\n",
    "    if hline:\n",
    "        # Add a custom horizontal line at y = 2 (you can adjust the y-value as needed)\n",
    "        plt.axhline(y=hline_y, color=hline_color, linestyle='--', label=hline_label)\n",
    "        plt.legend()\n",
    "        \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cb85e3af-4799-4408-a338-2803f4135e1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets have a look at the complete dataset with all the indicators included, what we see is that 2022 have relatively many missing values compared to the majority of the dataset.\n",
    "# In literature having less than 5% missing values is acceptable but for sake of demonstration we will show how they are dropped. Despite the low amount of missing values there could be some change that we want to leave out.\n",
    "\n",
    "# Calculate the number of missing values per column\n",
    "missing_values = df.isnull().sum()\n",
    "missing_plot(missing_values, df.shape[0], '% Missing values per Column', hline_y=1, hline=True, hline_label='Threshold at 1%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c0afcb79-dc45-4cac-995f-203714274df8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here is visualized how many missing values per indicator summed over all columns exist. This also provides us with insights on how many missing values we deem acceptable.\n",
    "# For the visualization we want to use the indicators with minimal missing values, for reference a hline of 2000 is also plotted.\n",
    "#\n",
    "# The indicator that will be used for the GeoData Visualization is:\n",
    "# SP.RUR.TOTL.ZS\n",
    "\n",
    "group_by_indicator = df.groupby('Indicator Code')\n",
    "missing_values_per_indicator = (group_by_indicator.apply(lambda x: x.isna().sum()))\n",
    "missing_values_per_indicator_t = missing_values_per_indicator.transpose().sum()\n",
    "missing_values_per_indicator_t_sorted = missing_values_per_indicator_t.sort_values()\n",
    "missing_plot(missing_values_per_indicator_t_sorted,df.shape[0],'Missing values grouped by Indicator',xlabel='Indicator Codes',hline=True,hline_y=1,hline_label='Threshold at 1%')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "103a2f1e-e715-4e3e-b4c5-0531d6d5c23a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cleaning up the dataframe for obsolete columns after selection has been performed.\n",
    "indicator_keep = 'SP.RUR.TOTL.ZS'\n",
    "indicator_codes_unique = df['Indicator Code'].unique()\n",
    "drop_indicators = [x for x in indicator_codes_unique if x not in indicator_keep]\n",
    "\n",
    "# Use boolean indexing to select rows to drop and then use drop method\n",
    "df.drop(df[df['Indicator Code'].isin(drop_indicators)].index, inplace=True)\n",
    "\n",
    "# Check if the updated dataframe only contains the desired indicators\n",
    "df['Indicator Code'].unique()\n",
    "\n",
    "# Calculate the number of missing values per column\n",
    "missing_values = df.isnull().sum()\n",
    "missing_plot(missing_values,df.shape[0],'Missing values per Column',hline_y=0.5,hline=True,hline_label='Threshold at 0.5%')\n",
    "\n",
    "# Dropping the year column that was mentioned earlier with too many missing values which is 2022\n",
    "df.drop(columns=['2022'],inplace=True)\n",
    "\n",
    "# Reindexing dataframe\n",
    "df.reset_index(inplace=True,drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6e605b23-9598-42b9-9328-9fb6c7937997",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Indicator Name</th>\n",
       "      <th>Indicator Code</th>\n",
       "      <th>1960</th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>1964</th>\n",
       "      <th>1965</th>\n",
       "      <th>...</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>Rural population (% of total population)</td>\n",
       "      <td>SP.RUR.TOTL.ZS</td>\n",
       "      <td>4.922400e+01</td>\n",
       "      <td>4.923900e+01</td>\n",
       "      <td>4.925400e+01</td>\n",
       "      <td>4.927000e+01</td>\n",
       "      <td>4.928500e+01</td>\n",
       "      <td>4.930000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>5.704300e+01</td>\n",
       "      <td>5.701000e+01</td>\n",
       "      <td>5.695900e+01</td>\n",
       "      <td>5.689200e+01</td>\n",
       "      <td>5.680800e+01</td>\n",
       "      <td>5.670700e+01</td>\n",
       "      <td>5.658900e+01</td>\n",
       "      <td>5.645400e+01</td>\n",
       "      <td>5.630300e+01</td>\n",
       "      <td>5.613400e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>Rural population</td>\n",
       "      <td>SP.RUR.TOTL</td>\n",
       "      <td>2.688000e+04</td>\n",
       "      <td>2.748100e+04</td>\n",
       "      <td>2.791800e+04</td>\n",
       "      <td>2.831800e+04</td>\n",
       "      <td>2.867300e+04</td>\n",
       "      <td>2.898000e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>5.824800e+04</td>\n",
       "      <td>5.865200e+04</td>\n",
       "      <td>5.900600e+04</td>\n",
       "      <td>5.931400e+04</td>\n",
       "      <td>5.957700e+04</td>\n",
       "      <td>5.979100e+04</td>\n",
       "      <td>5.996300e+04</td>\n",
       "      <td>6.009100e+04</td>\n",
       "      <td>6.001100e+04</td>\n",
       "      <td>5.980300e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Africa Eastern and Southern</td>\n",
       "      <td>AFE</td>\n",
       "      <td>Rural population (% of total population)</td>\n",
       "      <td>SP.RUR.TOTL.ZS</td>\n",
       "      <td>8.543619e+01</td>\n",
       "      <td>8.518859e+01</td>\n",
       "      <td>8.493075e+01</td>\n",
       "      <td>8.465202e+01</td>\n",
       "      <td>8.435981e+01</td>\n",
       "      <td>8.405872e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>6.695953e+01</td>\n",
       "      <td>6.652379e+01</td>\n",
       "      <td>6.606304e+01</td>\n",
       "      <td>6.557442e+01</td>\n",
       "      <td>6.510525e+01</td>\n",
       "      <td>6.464110e+01</td>\n",
       "      <td>6.415240e+01</td>\n",
       "      <td>6.366374e+01</td>\n",
       "      <td>6.317170e+01</td>\n",
       "      <td>6.267630e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Africa Eastern and Southern</td>\n",
       "      <td>AFE</td>\n",
       "      <td>Rural population</td>\n",
       "      <td>SP.RUR.TOTL</td>\n",
       "      <td>1.116588e+08</td>\n",
       "      <td>1.142969e+08</td>\n",
       "      <td>1.170648e+08</td>\n",
       "      <td>1.198931e+08</td>\n",
       "      <td>1.228329e+08</td>\n",
       "      <td>1.258715e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>3.699719e+08</td>\n",
       "      <td>3.777832e+08</td>\n",
       "      <td>3.855775e+08</td>\n",
       "      <td>3.934519e+08</td>\n",
       "      <td>4.012940e+08</td>\n",
       "      <td>4.090142e+08</td>\n",
       "      <td>4.168347e+08</td>\n",
       "      <td>4.247917e+08</td>\n",
       "      <td>4.327973e+08</td>\n",
       "      <td>4.405999e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Rural population (% of total population)</td>\n",
       "      <td>SP.RUR.TOTL.ZS</td>\n",
       "      <td>9.159900e+01</td>\n",
       "      <td>9.131600e+01</td>\n",
       "      <td>9.102400e+01</td>\n",
       "      <td>9.072400e+01</td>\n",
       "      <td>9.041400e+01</td>\n",
       "      <td>9.009600e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>7.584000e+01</td>\n",
       "      <td>7.562700e+01</td>\n",
       "      <td>7.541300e+01</td>\n",
       "      <td>7.519700e+01</td>\n",
       "      <td>7.498000e+01</td>\n",
       "      <td>7.475000e+01</td>\n",
       "      <td>7.450500e+01</td>\n",
       "      <td>7.424600e+01</td>\n",
       "      <td>7.397400e+01</td>\n",
       "      <td>7.368600e+01</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",
       "      <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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>Rural population</td>\n",
       "      <td>SP.RUR.TOTL</td>\n",
       "      <td>8.818777e+06</td>\n",
       "      <td>9.039584e+06</td>\n",
       "      <td>9.293113e+06</td>\n",
       "      <td>9.558766e+06</td>\n",
       "      <td>9.834713e+06</td>\n",
       "      <td>1.012163e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>1.951911e+07</td>\n",
       "      <td>1.950602e+07</td>\n",
       "      <td>1.953188e+07</td>\n",
       "      <td>1.965288e+07</td>\n",
       "      <td>1.955540e+07</td>\n",
       "      <td>1.934297e+07</td>\n",
       "      <td>1.929192e+07</td>\n",
       "      <td>1.925237e+07</td>\n",
       "      <td>1.919648e+07</td>\n",
       "      <td>1.909639e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>Rural population (% of total population)</td>\n",
       "      <td>SP.RUR.TOTL.ZS</td>\n",
       "      <td>8.185500e+01</td>\n",
       "      <td>8.104900e+01</td>\n",
       "      <td>8.021500e+01</td>\n",
       "      <td>7.928800e+01</td>\n",
       "      <td>7.798500e+01</td>\n",
       "      <td>7.662800e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>5.964600e+01</td>\n",
       "      <td>5.913500e+01</td>\n",
       "      <td>5.861800e+01</td>\n",
       "      <td>5.809300e+01</td>\n",
       "      <td>5.756200e+01</td>\n",
       "      <td>5.702400e+01</td>\n",
       "      <td>5.647900e+01</td>\n",
       "      <td>5.592800e+01</td>\n",
       "      <td>5.537100e+01</td>\n",
       "      <td>5.480800e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>Rural population</td>\n",
       "      <td>SP.RUR.TOTL</td>\n",
       "      <td>2.553409e+06</td>\n",
       "      <td>2.609333e+06</td>\n",
       "      <td>2.665887e+06</td>\n",
       "      <td>2.720673e+06</td>\n",
       "      <td>2.762825e+06</td>\n",
       "      <td>2.803071e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>8.794599e+06</td>\n",
       "      <td>9.009203e+06</td>\n",
       "      <td>9.225180e+06</td>\n",
       "      <td>9.439084e+06</td>\n",
       "      <td>9.651859e+06</td>\n",
       "      <td>9.864042e+06</td>\n",
       "      <td>1.007353e+07</td>\n",
       "      <td>1.027983e+07</td>\n",
       "      <td>1.048046e+07</td>\n",
       "      <td>1.067283e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Rural population (% of total population)</td>\n",
       "      <td>SP.RUR.TOTL.ZS</td>\n",
       "      <td>8.739200e+01</td>\n",
       "      <td>8.717900e+01</td>\n",
       "      <td>8.691800e+01</td>\n",
       "      <td>8.642200e+01</td>\n",
       "      <td>8.590800e+01</td>\n",
       "      <td>8.538000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>6.716600e+01</td>\n",
       "      <td>6.734600e+01</td>\n",
       "      <td>6.749600e+01</td>\n",
       "      <td>6.761500e+01</td>\n",
       "      <td>6.770400e+01</td>\n",
       "      <td>6.776300e+01</td>\n",
       "      <td>6.779100e+01</td>\n",
       "      <td>6.779000e+01</td>\n",
       "      <td>6.775800e+01</td>\n",
       "      <td>6.769700e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Rural population</td>\n",
       "      <td>SP.RUR.TOTL</td>\n",
       "      <td>3.326410e+06</td>\n",
       "      <td>3.422606e+06</td>\n",
       "      <td>3.519986e+06</td>\n",
       "      <td>3.610652e+06</td>\n",
       "      <td>3.702920e+06</td>\n",
       "      <td>3.796976e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>8.909792e+06</td>\n",
       "      <td>9.129035e+06</td>\n",
       "      <td>9.352079e+06</td>\n",
       "      <td>9.570861e+06</td>\n",
       "      <td>9.785059e+06</td>\n",
       "      <td>9.995789e+06</td>\n",
       "      <td>1.020403e+07</td>\n",
       "      <td>1.040889e+07</td>\n",
       "      <td>1.061745e+07</td>\n",
       "      <td>1.082714e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>532 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Country Name Country Code  \\\n",
       "0                          Aruba          ABW   \n",
       "1                          Aruba          ABW   \n",
       "2    Africa Eastern and Southern          AFE   \n",
       "3    Africa Eastern and Southern          AFE   \n",
       "4                    Afghanistan          AFG   \n",
       "..                           ...          ...   \n",
       "527                 South Africa          ZAF   \n",
       "528                       Zambia          ZMB   \n",
       "529                       Zambia          ZMB   \n",
       "530                     Zimbabwe          ZWE   \n",
       "531                     Zimbabwe          ZWE   \n",
       "\n",
       "                               Indicator Name  Indicator Code          1960  \\\n",
       "0    Rural population (% of total population)  SP.RUR.TOTL.ZS  4.922400e+01   \n",
       "1                            Rural population     SP.RUR.TOTL  2.688000e+04   \n",
       "2    Rural population (% of total population)  SP.RUR.TOTL.ZS  8.543619e+01   \n",
       "3                            Rural population     SP.RUR.TOTL  1.116588e+08   \n",
       "4    Rural population (% of total population)  SP.RUR.TOTL.ZS  9.159900e+01   \n",
       "..                                        ...             ...           ...   \n",
       "527                          Rural population     SP.RUR.TOTL  8.818777e+06   \n",
       "528  Rural population (% of total population)  SP.RUR.TOTL.ZS  8.185500e+01   \n",
       "529                          Rural population     SP.RUR.TOTL  2.553409e+06   \n",
       "530  Rural population (% of total population)  SP.RUR.TOTL.ZS  8.739200e+01   \n",
       "531                          Rural population     SP.RUR.TOTL  3.326410e+06   \n",
       "\n",
       "             1961          1962          1963          1964          1965  \\\n",
       "0    4.923900e+01  4.925400e+01  4.927000e+01  4.928500e+01  4.930000e+01   \n",
       "1    2.748100e+04  2.791800e+04  2.831800e+04  2.867300e+04  2.898000e+04   \n",
       "2    8.518859e+01  8.493075e+01  8.465202e+01  8.435981e+01  8.405872e+01   \n",
       "3    1.142969e+08  1.170648e+08  1.198931e+08  1.228329e+08  1.258715e+08   \n",
       "4    9.131600e+01  9.102400e+01  9.072400e+01  9.041400e+01  9.009600e+01   \n",
       "..            ...           ...           ...           ...           ...   \n",
       "527  9.039584e+06  9.293113e+06  9.558766e+06  9.834713e+06  1.012163e+07   \n",
       "528  8.104900e+01  8.021500e+01  7.928800e+01  7.798500e+01  7.662800e+01   \n",
       "529  2.609333e+06  2.665887e+06  2.720673e+06  2.762825e+06  2.803071e+06   \n",
       "530  8.717900e+01  8.691800e+01  8.642200e+01  8.590800e+01  8.538000e+01   \n",
       "531  3.422606e+06  3.519986e+06  3.610652e+06  3.702920e+06  3.796976e+06   \n",
       "\n",
       "     ...          2012          2013          2014          2015  \\\n",
       "0    ...  5.704300e+01  5.701000e+01  5.695900e+01  5.689200e+01   \n",
       "1    ...  5.824800e+04  5.865200e+04  5.900600e+04  5.931400e+04   \n",
       "2    ...  6.695953e+01  6.652379e+01  6.606304e+01  6.557442e+01   \n",
       "3    ...  3.699719e+08  3.777832e+08  3.855775e+08  3.934519e+08   \n",
       "4    ...  7.584000e+01  7.562700e+01  7.541300e+01  7.519700e+01   \n",
       "..   ...           ...           ...           ...           ...   \n",
       "527  ...  1.951911e+07  1.950602e+07  1.953188e+07  1.965288e+07   \n",
       "528  ...  5.964600e+01  5.913500e+01  5.861800e+01  5.809300e+01   \n",
       "529  ...  8.794599e+06  9.009203e+06  9.225180e+06  9.439084e+06   \n",
       "530  ...  6.716600e+01  6.734600e+01  6.749600e+01  6.761500e+01   \n",
       "531  ...  8.909792e+06  9.129035e+06  9.352079e+06  9.570861e+06   \n",
       "\n",
       "             2016          2017          2018          2019          2020  \\\n",
       "0    5.680800e+01  5.670700e+01  5.658900e+01  5.645400e+01  5.630300e+01   \n",
       "1    5.957700e+04  5.979100e+04  5.996300e+04  6.009100e+04  6.001100e+04   \n",
       "2    6.510525e+01  6.464110e+01  6.415240e+01  6.366374e+01  6.317170e+01   \n",
       "3    4.012940e+08  4.090142e+08  4.168347e+08  4.247917e+08  4.327973e+08   \n",
       "4    7.498000e+01  7.475000e+01  7.450500e+01  7.424600e+01  7.397400e+01   \n",
       "..            ...           ...           ...           ...           ...   \n",
       "527  1.955540e+07  1.934297e+07  1.929192e+07  1.925237e+07  1.919648e+07   \n",
       "528  5.756200e+01  5.702400e+01  5.647900e+01  5.592800e+01  5.537100e+01   \n",
       "529  9.651859e+06  9.864042e+06  1.007353e+07  1.027983e+07  1.048046e+07   \n",
       "530  6.770400e+01  6.776300e+01  6.779100e+01  6.779000e+01  6.775800e+01   \n",
       "531  9.785059e+06  9.995789e+06  1.020403e+07  1.040889e+07  1.061745e+07   \n",
       "\n",
       "             2021  \n",
       "0    5.613400e+01  \n",
       "1    5.980300e+04  \n",
       "2    6.267630e+01  \n",
       "3    4.405999e+08  \n",
       "4    7.368600e+01  \n",
       "..            ...  \n",
       "527  1.909639e+07  \n",
       "528  5.480800e+01  \n",
       "529  1.067283e+07  \n",
       "530  6.769700e+01  \n",
       "531  1.082714e+07  \n",
       "\n",
       "[532 rows x 66 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The cleaned dataset after selecting appropiate indicators\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "20238a54-d380-45ba-9254-626c8fbabe71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current shape of dataframe (532, 66)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the number of missing values per column - which has been reduced to below o.5% per column on average\n",
    "print('Current shape of dataframe',df.shape)\n",
    "missing_values = df.isnull().sum()\n",
    "missing_plot(missing_values,df.shape[0],'Missing value % per Column',ylabel='% of Missing Values',hline_y=0.5,hline=False,hline_label='Threshold at 0.5%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aa421d5c-de6b-4a46-8acf-5f5698aeb3b5",
   "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>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Indicator Name</th>\n",
       "      <th>Indicator Code</th>\n",
       "      <th>1960</th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>1964</th>\n",
       "      <th>1965</th>\n",
       "      <th>...</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>Not classified</td>\n",
       "      <td>INX</td>\n",
       "      <td>Rural population (% of total population)</td>\n",
       "      <td>SP.RUR.TOTL.ZS</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>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>Not classified</td>\n",
       "      <td>INX</td>\n",
       "      <td>Rural population</td>\n",
       "      <td>SP.RUR.TOTL</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>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>St. Martin (French part)</td>\n",
       "      <td>MAF</td>\n",
       "      <td>Rural population (% of total population)</td>\n",
       "      <td>SP.RUR.TOTL.ZS</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>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>St. Martin (French part)</td>\n",
       "      <td>MAF</td>\n",
       "      <td>Rural population</td>\n",
       "      <td>SP.RUR.TOTL</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>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>PSE</td>\n",
       "      <td>Rural population</td>\n",
       "      <td>SP.RUR.TOTL</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>...</td>\n",
       "      <td>1010163.0</td>\n",
       "      <td>1024762.0</td>\n",
       "      <td>1038634.0</td>\n",
       "      <td>1051809.0</td>\n",
       "      <td>1064347.0</td>\n",
       "      <td>1073875.0</td>\n",
       "      <td>1089088.0</td>\n",
       "      <td>1103858.0</td>\n",
       "      <td>1118249.0</td>\n",
       "      <td>1132085.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>Kosovo</td>\n",
       "      <td>XKX</td>\n",
       "      <td>Rural population (% of total population)</td>\n",
       "      <td>SP.RUR.TOTL.ZS</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>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>Kosovo</td>\n",
       "      <td>XKX</td>\n",
       "      <td>Rural population</td>\n",
       "      <td>SP.RUR.TOTL</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>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Country Name Country Code  \\\n",
       "220            Not classified          INX   \n",
       "221            Not classified          INX   \n",
       "294  St. Martin (French part)          MAF   \n",
       "295  St. Martin (French part)          MAF   \n",
       "393        West Bank and Gaza          PSE   \n",
       "522                    Kosovo          XKX   \n",
       "523                    Kosovo          XKX   \n",
       "\n",
       "                               Indicator Name  Indicator Code  1960  1961  \\\n",
       "220  Rural population (% of total population)  SP.RUR.TOTL.ZS   NaN   NaN   \n",
       "221                          Rural population     SP.RUR.TOTL   NaN   NaN   \n",
       "294  Rural population (% of total population)  SP.RUR.TOTL.ZS   NaN   NaN   \n",
       "295                          Rural population     SP.RUR.TOTL   NaN   NaN   \n",
       "393                          Rural population     SP.RUR.TOTL   NaN   NaN   \n",
       "522  Rural population (% of total population)  SP.RUR.TOTL.ZS   NaN   NaN   \n",
       "523                          Rural population     SP.RUR.TOTL   NaN   NaN   \n",
       "\n",
       "     1962  1963  1964  1965  ...       2012       2013       2014       2015  \\\n",
       "220   NaN   NaN   NaN   NaN  ...        NaN        NaN        NaN        NaN   \n",
       "221   NaN   NaN   NaN   NaN  ...        NaN        NaN        NaN        NaN   \n",
       "294   NaN   NaN   NaN   NaN  ...        NaN        NaN        NaN        NaN   \n",
       "295   NaN   NaN   NaN   NaN  ...        NaN        NaN        NaN        NaN   \n",
       "393   NaN   NaN   NaN   NaN  ...  1010163.0  1024762.0  1038634.0  1051809.0   \n",
       "522   NaN   NaN   NaN   NaN  ...        NaN        NaN        NaN        NaN   \n",
       "523   NaN   NaN   NaN   NaN  ...        NaN        NaN        NaN        NaN   \n",
       "\n",
       "          2016       2017       2018       2019       2020       2021  \n",
       "220        NaN        NaN        NaN        NaN        NaN        NaN  \n",
       "221        NaN        NaN        NaN        NaN        NaN        NaN  \n",
       "294        NaN        NaN        NaN        NaN        NaN        NaN  \n",
       "295        NaN        NaN        NaN        NaN        NaN        NaN  \n",
       "393  1064347.0  1073875.0  1089088.0  1103858.0  1118249.0  1132085.0  \n",
       "522        NaN        NaN        NaN        NaN        NaN        NaN  \n",
       "523        NaN        NaN        NaN        NaN        NaN        NaN  \n",
       "\n",
       "[7 rows x 66 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remaining missing value data e.g. for 1960\n",
    "is_missing = df[df['1960'].isna()].index\n",
    "df[df['1960'].isna()].head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96f9df4d",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d443ac92-6633-4c24-9cde-53765cab46ec",
   "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>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Indicator Name</th>\n",
       "      <th>Indicator Code</th>\n",
       "      <th>1960</th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>1964</th>\n",
       "      <th>1965</th>\n",
       "      <th>...</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>Not classified</td>\n",
       "      <td>INX</td>\n",
       "      <td>Rural population (% of total population)</td>\n",
       "      <td>SP.RUR.TOTL.ZS</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>...</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",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>Not classified</td>\n",
       "      <td>INX</td>\n",
       "      <td>Rural population</td>\n",
       "      <td>SP.RUR.TOTL</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>...</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",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>St. Martin (French part)</td>\n",
       "      <td>MAF</td>\n",
       "      <td>Rural population (% of total population)</td>\n",
       "      <td>SP.RUR.TOTL.ZS</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>...</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",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>St. Martin (French part)</td>\n",
       "      <td>MAF</td>\n",
       "      <td>Rural population</td>\n",
       "      <td>SP.RUR.TOTL</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>...</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",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>PSE</td>\n",
       "      <td>Rural population</td>\n",
       "      <td>SP.RUR.TOTL</td>\n",
       "      <td>638796.0</td>\n",
       "      <td>638796.0</td>\n",
       "      <td>638796.0</td>\n",
       "      <td>638796.0</td>\n",
       "      <td>638796.0</td>\n",
       "      <td>638796.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1010163.0</td>\n",
       "      <td>1024762.0</td>\n",
       "      <td>1038634.0</td>\n",
       "      <td>1051809.0</td>\n",
       "      <td>1064347.0</td>\n",
       "      <td>1073875.0</td>\n",
       "      <td>1089088.0</td>\n",
       "      <td>1103858.0</td>\n",
       "      <td>1118249.0</td>\n",
       "      <td>1132085.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>Kosovo</td>\n",
       "      <td>XKX</td>\n",
       "      <td>Rural population (% of total population)</td>\n",
       "      <td>SP.RUR.TOTL.ZS</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>...</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",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>Kosovo</td>\n",
       "      <td>XKX</td>\n",
       "      <td>Rural population</td>\n",
       "      <td>SP.RUR.TOTL</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>...</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",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Country Name Country Code  \\\n",
       "220            Not classified          INX   \n",
       "221            Not classified          INX   \n",
       "294  St. Martin (French part)          MAF   \n",
       "295  St. Martin (French part)          MAF   \n",
       "393        West Bank and Gaza          PSE   \n",
       "522                    Kosovo          XKX   \n",
       "523                    Kosovo          XKX   \n",
       "\n",
       "                               Indicator Name  Indicator Code      1960  \\\n",
       "220  Rural population (% of total population)  SP.RUR.TOTL.ZS       0.0   \n",
       "221                          Rural population     SP.RUR.TOTL       0.0   \n",
       "294  Rural population (% of total population)  SP.RUR.TOTL.ZS       0.0   \n",
       "295                          Rural population     SP.RUR.TOTL       0.0   \n",
       "393                          Rural population     SP.RUR.TOTL  638796.0   \n",
       "522  Rural population (% of total population)  SP.RUR.TOTL.ZS       0.0   \n",
       "523                          Rural population     SP.RUR.TOTL       0.0   \n",
       "\n",
       "         1961      1962      1963      1964      1965  ...       2012  \\\n",
       "220       0.0       0.0       0.0       0.0       0.0  ...        0.0   \n",
       "221       0.0       0.0       0.0       0.0       0.0  ...        0.0   \n",
       "294       0.0       0.0       0.0       0.0       0.0  ...        0.0   \n",
       "295       0.0       0.0       0.0       0.0       0.0  ...        0.0   \n",
       "393  638796.0  638796.0  638796.0  638796.0  638796.0  ...  1010163.0   \n",
       "522       0.0       0.0       0.0       0.0       0.0  ...        0.0   \n",
       "523       0.0       0.0       0.0       0.0       0.0  ...        0.0   \n",
       "\n",
       "          2013       2014       2015       2016       2017       2018  \\\n",
       "220        0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "221        0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "294        0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "295        0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "393  1024762.0  1038634.0  1051809.0  1064347.0  1073875.0  1089088.0   \n",
       "522        0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "523        0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "\n",
       "          2019       2020       2021  \n",
       "220        0.0        0.0        0.0  \n",
       "221        0.0        0.0        0.0  \n",
       "294        0.0        0.0        0.0  \n",
       "295        0.0        0.0        0.0  \n",
       "393  1103858.0  1118249.0  1132085.0  \n",
       "522        0.0        0.0        0.0  \n",
       "523        0.0        0.0        0.0  \n",
       "\n",
       "[7 rows x 66 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting all year column indexes\n",
    "years = df.columns[4:]\n",
    "\n",
    "# Replace missing values with 0, in visualization if the value is exactly 0 we can show it as missing.\n",
    "# Check if any of the year columns have non-NaN values\n",
    "if df[years].notna().any().any():\n",
    "    # At least one column has non-NaN values\n",
    "    df[years] = df[years].interpolate(method='linear', axis=1, limit_direction='both')\n",
    "\n",
    "# All year columns have NaN values\n",
    "df.fillna(0,inplace=True)\n",
    "\n",
    "# Show updated dataframe for the example of 1960 indexes\n",
    "df.iloc[is_missing].head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4fa4e946-1f48-4f47-8647-13519d7f934d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For visualization purposes we will add column that contains the continent of each country based on the ISO_A3 code.\n",
    "continents = []\n",
    "\n",
    "for ISO_A3 in df['Country Code']:\n",
    "    found = False  # Flag to check if a match is found\n",
    "    for continent, codes in cleaned_iso.items():\n",
    "        add = None\n",
    "        if ISO_A3 in codes:\n",
    "            if continent == 'Seven seas (open ocean)':\n",
    "                add = 'Seven seas'\n",
    "            else:\n",
    "                add = continent\n",
    "            continents.append(add)\n",
    "            found = True  # Set the flag to True when a match is found\n",
    "            break  # Exit the loop when a match is found\n",
    "    if not found:\n",
    "        continents.append('Unclassified')\n",
    "\n",
    "df.insert(2, 'Continent', continents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6389aebe-e797-4463-868e-f0a181c79aa9",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Africa',\n",
       " 'Asia',\n",
       " 'Europe',\n",
       " 'North America',\n",
       " 'Oceania',\n",
       " 'Seven seas',\n",
       " 'South America',\n",
       " 'Unclassified']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Continent has been added to the dataframe at column index 2\n",
    "continents = sorted(df['Continent'].unique())\n",
    "continents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a7bd2175",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2        Africa Eastern and Southern\n",
      "3        Africa Eastern and Southern\n",
      "6         Africa Western and Central\n",
      "7         Africa Western and Central\n",
      "14                        Arab World\n",
      "15                        Arab World\n",
      "72    Central Europe and the Baltics\n",
      "73    Central Europe and the Baltics\n",
      "76                   Channel Islands\n",
      "77                   Channel Islands\n",
      "Name: Country Name, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# The 53 Unclassified countries which ISO_A3 code was not found have to be removed or updated to belong to the correct continent\n",
    "# Many here are aggregated values, we will manually select countries that will be updated.\n",
    "unclassified_codes = df[df['Continent'] == 'Unclassified']['Country Code']\n",
    "\n",
    "# France FRA Europe\n",
    "# Norway NOR Europe\n",
    "# Channel Islands CHI Europe\n",
    "# Kosovo XKX Europe\n",
    "\n",
    "print(df[df['Continent'] == 'Unclassified']['Country Name'].head(10))\n",
    "\n",
    "keep_unclassified  = ['FRA','NOR','CHI', 'XKX']\n",
    "\n",
    "# Update the continent for the specified country codes to 'Europe'\n",
    "df.loc[df['Country Code'].isin(keep_unclassified), 'Continent'] = 'Europe'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "56739d91",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Proceed to drop all rows from unclassified which do not include 'FRA','NOR','CHI', 'XKX'\n",
    "drop_unclassified_filtered = df['Country Code'].isin([code for code in unclassified_codes if code not in keep_unclassified])\n",
    "# drop_unclassified_filtered\n",
    "df = df.drop(df[drop_unclassified_filtered].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "46054c37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now that the dataset is cleaned and ready for visualization lets first do a final descriptive statistic exploration and visualizations.\n",
    "mask_rural_pcnt = df['Indicator Code']  == 'SP.RUR.TOTL.ZS'\n",
    "mask_rural_abs = df['Indicator Code']  == 'SP.RUR.TOTL'\n",
    "# Set the float format to a fixed number of decimal places (e.g., 2)\n",
    "pd.options.display.float_format = '{:.2f}'.format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d4c8e424",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 4600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# In this summary we focus on Rural Population Percentage after the data is cleaned. We can clearly see a trend over the years, including the max, min, mean, std and quantiles.\n",
    "summary_stats = df[mask_rural_pcnt].describe()\n",
    "summary_stats = summary_stats\n",
    "\n",
    "# Create a custom heatmap with a color gradient\n",
    "plt.figure(figsize=(46, 8))\n",
    "\n",
    "# Define a color map that spans the entire data range\n",
    "# cmap = sns.diverging_palette(220, 0, sep=20, as_cmap=True)\n",
    "\n",
    "# Create the heatmap using a diverging color map\n",
    "sns.heatmap(data=summary_stats, annot=True, fmt=\".1f\", cmap=plt.cm.coolwarm, linewidths=0.5, cbar=True, vmin=0, vmax=100)\n",
    "\n",
    "plt.title(\"Custom Summary Statistics Heatmap\")\n",
    "plt.show()\n",
    "\n",
    "# Interesting trends become appearent like a continuous downtrend for the mean for all years.\n",
    "# The standard deviation seems stable from 1960 to 1971, then it increases slightly till 1982 and start to drop again continuously until 2021.\n",
    "# Convergence might indicate that the differences in rural population percentages between regions or countries are diminishing over time. In other words, regions that previously had very different rural population percentages are becoming more similar.\n",
    "# A convergence in rural population percentages can be a sign of urbanization. As urbanization progresses, more people move from rural to urban areas, which can lead to a decrease in the rural population percentage across regions or countries.\n",
    "# It could be associated with economic development. More economically developed regions or countries often experience a shift from agriculture-based economies \n",
    "# (which tend to have higher rural populations) to more industrial or service-based economies (which tend to have lower rural populations).\n",
    "# Changes in government policies, rural development programs, and social trends can influence the rural population percentages. Convergence may indicate that such policies and trends are leading to more consistent living patterns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "449a5829",
   "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>1960</th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>1964</th>\n",
       "      <th>1965</th>\n",
       "      <th>1966</th>\n",
       "      <th>1967</th>\n",
       "      <th>1968</th>\n",
       "      <th>1969</th>\n",
       "      <th>...</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>...</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>61.91</td>\n",
       "      <td>61.39</td>\n",
       "      <td>60.85</td>\n",
       "      <td>60.30</td>\n",
       "      <td>59.75</td>\n",
       "      <td>59.18</td>\n",
       "      <td>58.62</td>\n",
       "      <td>58.05</td>\n",
       "      <td>57.47</td>\n",
       "      <td>56.88</td>\n",
       "      <td>...</td>\n",
       "      <td>40.55</td>\n",
       "      <td>40.27</td>\n",
       "      <td>39.99</td>\n",
       "      <td>39.71</td>\n",
       "      <td>39.42</td>\n",
       "      <td>39.13</td>\n",
       "      <td>38.83</td>\n",
       "      <td>38.53</td>\n",
       "      <td>38.23</td>\n",
       "      <td>37.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>25.51</td>\n",
       "      <td>25.52</td>\n",
       "      <td>25.54</td>\n",
       "      <td>25.56</td>\n",
       "      <td>25.57</td>\n",
       "      <td>25.58</td>\n",
       "      <td>25.59</td>\n",
       "      <td>25.60</td>\n",
       "      <td>25.61</td>\n",
       "      <td>25.62</td>\n",
       "      <td>...</td>\n",
       "      <td>24.41</td>\n",
       "      <td>24.38</td>\n",
       "      <td>24.34</td>\n",
       "      <td>24.30</td>\n",
       "      <td>24.25</td>\n",
       "      <td>24.20</td>\n",
       "      <td>24.15</td>\n",
       "      <td>24.09</td>\n",
       "      <td>24.03</td>\n",
       "      <td>23.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>46.27</td>\n",
       "      <td>46.28</td>\n",
       "      <td>45.54</td>\n",
       "      <td>44.23</td>\n",
       "      <td>43.93</td>\n",
       "      <td>42.43</td>\n",
       "      <td>41.56</td>\n",
       "      <td>41.15</td>\n",
       "      <td>40.72</td>\n",
       "      <td>40.05</td>\n",
       "      <td>...</td>\n",
       "      <td>21.30</td>\n",
       "      <td>20.94</td>\n",
       "      <td>20.61</td>\n",
       "      <td>20.34</td>\n",
       "      <td>20.08</td>\n",
       "      <td>19.72</td>\n",
       "      <td>19.22</td>\n",
       "      <td>18.90</td>\n",
       "      <td>18.59</td>\n",
       "      <td>18.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>65.84</td>\n",
       "      <td>65.49</td>\n",
       "      <td>64.76</td>\n",
       "      <td>64.08</td>\n",
       "      <td>63.26</td>\n",
       "      <td>62.86</td>\n",
       "      <td>62.25</td>\n",
       "      <td>61.44</td>\n",
       "      <td>60.83</td>\n",
       "      <td>60.67</td>\n",
       "      <td>...</td>\n",
       "      <td>39.35</td>\n",
       "      <td>39.47</td>\n",
       "      <td>39.53</td>\n",
       "      <td>39.19</td>\n",
       "      <td>38.64</td>\n",
       "      <td>38.32</td>\n",
       "      <td>38.10</td>\n",
       "      <td>37.87</td>\n",
       "      <td>37.42</td>\n",
       "      <td>36.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>82.65</td>\n",
       "      <td>82.10</td>\n",
       "      <td>81.63</td>\n",
       "      <td>80.94</td>\n",
       "      <td>80.55</td>\n",
       "      <td>80.38</td>\n",
       "      <td>79.67</td>\n",
       "      <td>79.56</td>\n",
       "      <td>79.28</td>\n",
       "      <td>78.61</td>\n",
       "      <td>...</td>\n",
       "      <td>59.65</td>\n",
       "      <td>59.13</td>\n",
       "      <td>58.89</td>\n",
       "      <td>59.00</td>\n",
       "      <td>58.77</td>\n",
       "      <td>58.36</td>\n",
       "      <td>57.94</td>\n",
       "      <td>57.52</td>\n",
       "      <td>57.20</td>\n",
       "      <td>56.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>97.92</td>\n",
       "      <td>97.89</td>\n",
       "      <td>97.85</td>\n",
       "      <td>97.81</td>\n",
       "      <td>97.77</td>\n",
       "      <td>97.71</td>\n",
       "      <td>97.61</td>\n",
       "      <td>97.50</td>\n",
       "      <td>97.39</td>\n",
       "      <td>97.27</td>\n",
       "      <td>...</td>\n",
       "      <td>88.81</td>\n",
       "      <td>88.52</td>\n",
       "      <td>88.22</td>\n",
       "      <td>87.92</td>\n",
       "      <td>87.61</td>\n",
       "      <td>87.29</td>\n",
       "      <td>86.97</td>\n",
       "      <td>86.75</td>\n",
       "      <td>86.66</td>\n",
       "      <td>86.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        1960   1961   1962   1963   1964   1965   1966   1967   1968   1969  \\\n",
       "count 217.00 217.00 217.00 217.00 217.00 217.00 217.00 217.00 217.00 217.00   \n",
       "mean   61.91  61.39  60.85  60.30  59.75  59.18  58.62  58.05  57.47  56.88   \n",
       "std    25.51  25.52  25.54  25.56  25.57  25.58  25.59  25.60  25.61  25.62   \n",
       "min     0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   \n",
       "25%    46.27  46.28  45.54  44.23  43.93  42.43  41.56  41.15  40.72  40.05   \n",
       "50%    65.84  65.49  64.76  64.08  63.26  62.86  62.25  61.44  60.83  60.67   \n",
       "75%    82.65  82.10  81.63  80.94  80.55  80.38  79.67  79.56  79.28  78.61   \n",
       "max    97.92  97.89  97.85  97.81  97.77  97.71  97.61  97.50  97.39  97.27   \n",
       "\n",
       "       ...   2012   2013   2014   2015   2016   2017   2018   2019   2020  \\\n",
       "count  ... 217.00 217.00 217.00 217.00 217.00 217.00 217.00 217.00 217.00   \n",
       "mean   ...  40.55  40.27  39.99  39.71  39.42  39.13  38.83  38.53  38.23   \n",
       "std    ...  24.41  24.38  24.34  24.30  24.25  24.20  24.15  24.09  24.03   \n",
       "min    ...   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   \n",
       "25%    ...  21.30  20.94  20.61  20.34  20.08  19.72  19.22  18.90  18.59   \n",
       "50%    ...  39.35  39.47  39.53  39.19  38.64  38.32  38.10  37.87  37.42   \n",
       "75%    ...  59.65  59.13  58.89  59.00  58.77  58.36  57.94  57.52  57.20   \n",
       "max    ...  88.81  88.52  88.22  87.92  87.61  87.29  86.97  86.75  86.66   \n",
       "\n",
       "        2021  \n",
       "count 217.00  \n",
       "mean   37.92  \n",
       "std    23.97  \n",
       "min     0.00  \n",
       "25%    18.52  \n",
       "50%    36.78  \n",
       "75%    56.99  \n",
       "max    86.54  \n",
       "\n",
       "[8 rows x 62 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In this summary we focus on Rural Population Percentage after the data is cleaned. We can clearly see a trend over the years, including the max, min, mean, std and quantiles.\n",
    "df[mask_rural_pcnt].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9cf02c08",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot of global rural percentage trend\n",
    "plt.figure(figsize=(20, 5))  # Adjust the figure size as needed\n",
    "\n",
    "sns.boxplot(\n",
    "    data=df[mask_rural_pcnt],\n",
    ")\n",
    "plt.title(f'World - Rural Population Percentage')\n",
    "plt.ylabel('% People living in Rural Areas')\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel('Years')\n",
    "plt.ylim(0,100)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "78a9a988",
   "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>1960</th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>1964</th>\n",
       "      <th>1965</th>\n",
       "      <th>1966</th>\n",
       "      <th>1967</th>\n",
       "      <th>1968</th>\n",
       "      <th>1969</th>\n",
       "      <th>...</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>...</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>217.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9242237.37</td>\n",
       "      <td>9297597.40</td>\n",
       "      <td>9399710.51</td>\n",
       "      <td>9544114.64</td>\n",
       "      <td>9686403.48</td>\n",
       "      <td>9854133.05</td>\n",
       "      <td>10030812.91</td>\n",
       "      <td>10203743.68</td>\n",
       "      <td>10381140.81</td>\n",
       "      <td>10566198.54</td>\n",
       "      <td>...</td>\n",
       "      <td>15612661.88</td>\n",
       "      <td>15657248.08</td>\n",
       "      <td>15695498.09</td>\n",
       "      <td>15725747.31</td>\n",
       "      <td>15753037.37</td>\n",
       "      <td>15777295.42</td>\n",
       "      <td>15794266.93</td>\n",
       "      <td>15804329.86</td>\n",
       "      <td>15808653.40</td>\n",
       "      <td>15795307.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>45917118.87</td>\n",
       "      <td>45738424.96</td>\n",
       "      <td>46114811.48</td>\n",
       "      <td>46989459.07</td>\n",
       "      <td>47831962.01</td>\n",
       "      <td>48989428.08</td>\n",
       "      <td>50277660.80</td>\n",
       "      <td>51509854.56</td>\n",
       "      <td>52795218.65</td>\n",
       "      <td>54167220.47</td>\n",
       "      <td>...</td>\n",
       "      <td>75242808.02</td>\n",
       "      <td>75129858.68</td>\n",
       "      <td>74979015.57</td>\n",
       "      <td>74790449.38</td>\n",
       "      <td>74598175.38</td>\n",
       "      <td>74402213.10</td>\n",
       "      <td>74157059.42</td>\n",
       "      <td>73872982.73</td>\n",
       "      <td>73548797.71</td>\n",
       "      <td>73130431.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>95585.00</td>\n",
       "      <td>95285.00</td>\n",
       "      <td>97259.00</td>\n",
       "      <td>99793.00</td>\n",
       "      <td>102252.00</td>\n",
       "      <td>106504.00</td>\n",
       "      <td>109044.00</td>\n",
       "      <td>111320.00</td>\n",
       "      <td>113263.00</td>\n",
       "      <td>114563.00</td>\n",
       "      <td>...</td>\n",
       "      <td>189049.00</td>\n",
       "      <td>190952.00</td>\n",
       "      <td>193185.00</td>\n",
       "      <td>196468.00</td>\n",
       "      <td>197461.00</td>\n",
       "      <td>199529.00</td>\n",
       "      <td>201507.00</td>\n",
       "      <td>203532.00</td>\n",
       "      <td>201999.00</td>\n",
       "      <td>199225.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1484612.00</td>\n",
       "      <td>1495129.00</td>\n",
       "      <td>1520350.00</td>\n",
       "      <td>1555138.00</td>\n",
       "      <td>1600343.00</td>\n",
       "      <td>1579815.00</td>\n",
       "      <td>1578912.00</td>\n",
       "      <td>1617126.00</td>\n",
       "      <td>1616181.00</td>\n",
       "      <td>1570746.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1912076.00</td>\n",
       "      <td>1897456.00</td>\n",
       "      <td>1898380.00</td>\n",
       "      <td>1861795.00</td>\n",
       "      <td>1826891.00</td>\n",
       "      <td>1792408.00</td>\n",
       "      <td>1759939.00</td>\n",
       "      <td>1738221.00</td>\n",
       "      <td>1718119.00</td>\n",
       "      <td>1654279.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4798172.00</td>\n",
       "      <td>4853778.00</td>\n",
       "      <td>4854036.00</td>\n",
       "      <td>4903678.00</td>\n",
       "      <td>4953633.00</td>\n",
       "      <td>5019280.00</td>\n",
       "      <td>5055318.00</td>\n",
       "      <td>5106858.00</td>\n",
       "      <td>5122818.00</td>\n",
       "      <td>5128878.00</td>\n",
       "      <td>...</td>\n",
       "      <td>9751197.00</td>\n",
       "      <td>9681760.00</td>\n",
       "      <td>9590865.00</td>\n",
       "      <td>9535148.00</td>\n",
       "      <td>9651859.00</td>\n",
       "      <td>9778365.00</td>\n",
       "      <td>9995643.00</td>\n",
       "      <td>10279833.00</td>\n",
       "      <td>10376129.00</td>\n",
       "      <td>10221684.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>558984648.00</td>\n",
       "      <td>550002064.00</td>\n",
       "      <td>551084460.00</td>\n",
       "      <td>561172774.00</td>\n",
       "      <td>570563019.00</td>\n",
       "      <td>585836641.00</td>\n",
       "      <td>603653090.00</td>\n",
       "      <td>620353282.00</td>\n",
       "      <td>637762514.00</td>\n",
       "      <td>656497738.00</td>\n",
       "      <td>...</td>\n",
       "      <td>871315929.00</td>\n",
       "      <td>877931069.00</td>\n",
       "      <td>883907800.00</td>\n",
       "      <td>889270551.00</td>\n",
       "      <td>894450030.00</td>\n",
       "      <td>899185932.00</td>\n",
       "      <td>903131481.00</td>\n",
       "      <td>906325664.00</td>\n",
       "      <td>908684959.00</td>\n",
       "      <td>909384771.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              1960         1961         1962         1963         1964  \\\n",
       "count       217.00       217.00       217.00       217.00       217.00   \n",
       "mean    9242237.37   9297597.40   9399710.51   9544114.64   9686403.48   \n",
       "std    45917118.87  45738424.96  46114811.48  46989459.07  47831962.01   \n",
       "min           0.00         0.00         0.00         0.00         0.00   \n",
       "25%       95585.00     95285.00     97259.00     99793.00    102252.00   \n",
       "50%     1484612.00   1495129.00   1520350.00   1555138.00   1600343.00   \n",
       "75%     4798172.00   4853778.00   4854036.00   4903678.00   4953633.00   \n",
       "max   558984648.00 550002064.00 551084460.00 561172774.00 570563019.00   \n",
       "\n",
       "              1965         1966         1967         1968         1969  ...  \\\n",
       "count       217.00       217.00       217.00       217.00       217.00  ...   \n",
       "mean    9854133.05  10030812.91  10203743.68  10381140.81  10566198.54  ...   \n",
       "std    48989428.08  50277660.80  51509854.56  52795218.65  54167220.47  ...   \n",
       "min           0.00         0.00         0.00         0.00         0.00  ...   \n",
       "25%      106504.00    109044.00    111320.00    113263.00    114563.00  ...   \n",
       "50%     1579815.00   1578912.00   1617126.00   1616181.00   1570746.00  ...   \n",
       "75%     5019280.00   5055318.00   5106858.00   5122818.00   5128878.00  ...   \n",
       "max   585836641.00 603653090.00 620353282.00 637762514.00 656497738.00  ...   \n",
       "\n",
       "              2012         2013         2014         2015         2016  \\\n",
       "count       217.00       217.00       217.00       217.00       217.00   \n",
       "mean   15612661.88  15657248.08  15695498.09  15725747.31  15753037.37   \n",
       "std    75242808.02  75129858.68  74979015.57  74790449.38  74598175.38   \n",
       "min           0.00         0.00         0.00         0.00         0.00   \n",
       "25%      189049.00    190952.00    193185.00    196468.00    197461.00   \n",
       "50%     1912076.00   1897456.00   1898380.00   1861795.00   1826891.00   \n",
       "75%     9751197.00   9681760.00   9590865.00   9535148.00   9651859.00   \n",
       "max   871315929.00 877931069.00 883907800.00 889270551.00 894450030.00   \n",
       "\n",
       "              2017         2018         2019         2020         2021  \n",
       "count       217.00       217.00       217.00       217.00       217.00  \n",
       "mean   15777295.42  15794266.93  15804329.86  15808653.40  15795307.20  \n",
       "std    74402213.10  74157059.42  73872982.73  73548797.71  73130431.63  \n",
       "min           0.00         0.00         0.00         0.00         0.00  \n",
       "25%      199529.00    201507.00    203532.00    201999.00    199225.00  \n",
       "50%     1792408.00   1759939.00   1738221.00   1718119.00   1654279.00  \n",
       "75%     9778365.00   9995643.00  10279833.00  10376129.00  10221684.00  \n",
       "max   899185932.00 903131481.00 906325664.00 908684959.00 909384771.00  \n",
       "\n",
       "[8 rows x 62 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In this summary we focus on Total Rural Population  after the data is cleaned.\n",
    "# For the absolute values there is also a trend however big countries will skew the data and countries with small population will not contribute much.\n",
    "# This is also the reason why we use Rural Percentage in the interactive geo-spatial visualization later on.\n",
    "# Big numbers like these are also hard to show display in heatmap, scientific notation will not make it more pretty either.\n",
    "\n",
    "# It's hard to tell if the world population is increasing or that the rural population is growing making it even harder to draw conclusions from.\n",
    "# One thing that is interesting is the standard deviation, which tends to converge later on, meaning that the population is having less and less spread among the mean.\n",
    "# This trend can also be observed in the quantiles. The max value increases year on year.\n",
    "df[mask_rural_abs].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "724f54f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x1500 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# From now the focus will be on Rural Percentages since these absolute numbers of total population are hard to put into perspective.\n",
    "# Here is a boxplot showing the same but in a more visual trend.\n",
    "\n",
    "plt.figure(figsize=(20, 15))  # Adjust the figure size as needed\n",
    "num_continents = len(continents) - 1\n",
    "grouped_continents = df[mask_rural_pcnt].groupby(\"Continent\")\n",
    "\n",
    "# Calculate the number of rows based on the number of continents and two columns\n",
    "num_rows = (num_continents // 2) + (num_continents % 2)\n",
    "\n",
    "for i, continent in enumerate(continents, 1):\n",
    "    if continent == 'Unclassified':\n",
    "        continue\n",
    "    plt.subplot(num_rows, 2, i)  # Create a subplot for each continent in a grid with num_rows rows and 2 columns\n",
    "\n",
    "    # Create a boxplot for the current continent\n",
    "    sns.boxplot(\n",
    "        data=grouped_continents.get_group(continent)[years],\n",
    "    )\n",
    "    plt.title(f'Rural Population Percentage in {continent}')\n",
    "    plt.ylabel('')\n",
    "    plt.xticks(rotation=90)\n",
    "    plt.ylim(0,100)\n",
    "\n",
    "fig = plt.gcf()\n",
    "plt.tight_layout()  # Adjust subplot layout for better spacing\n",
    "plt.show()\n",
    "\n",
    "# For each continent a grouped boxplot is created, further revealing trends around the world.\n",
    "# All of them show a general downtrend in rural population %.\n",
    "# Africa is still has most people living in rural areas, most outliers also don't go as low as continents like Asia, Europe, Oceania.\n",
    "# Seaven Seas and South America also have less outliers to the downside.\n",
    "# Continents like Europa, Asia, North America and Oceania in general are more developed and larger populations live in Urban areas lowering the Rural Populuation percentages.\n",
    "# Seaven seas trend is very confined, mostly since it consists of few countries and most of them are small. This could be a reason for the low variation since changes are going slow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "47e2c2f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now that we understand the data better we can enhance the exploration by using the following dash-plotly interactive geospatial visualization.\n",
    "# Countries can be grouped by continent by hovering over the boxplot continents.\n",
    "# One can highlight a country by hovering over it in the chloropleth map and see more information by hovering over the different elements.\n",
    "# For more information please visit the github repositiory: https://github.com/DeusNexus/iu-exploratory-data-analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "51259250-7a49-43b6-b005-bef371027890",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run all three cells below and go to the localhost url where the dashboard is hosted.\n",
    "\n",
    "# Import necessary modules for creating the dashboard\n",
    "import dash\n",
    "from dash import dcc\n",
    "from dash import html\n",
    "from dash.dependencies import Input, Output\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "# Assuming you have a list of years defined somewhere in your code\n",
    "# Extract the minimum and maximum years from the list\n",
    "years = [int(year) for year in years]  # Convert the list of years to integers\n",
    "min_year = min(years)  # Find the minimum year\n",
    "max_year = max(years)  # Find the maximum year\n",
    "\n",
    "# Create a Dash web application\n",
    "app = dash.Dash(__name__)\n",
    "\n",
    "# Assuming you have a DataFrame 'df' and you want to filter it\n",
    "# to only include rows where 'Indicator Code' is 'SP.RUR.TOTL.ZS'\n",
    "df_rural = df[df['Indicator Code'] == 'SP.RUR.TOTL.ZS']\n",
    "\n",
    "# Calculate median values for each continent for the initial year (1960)\n",
    "median_values = df_rural.groupby(\"Continent\")[str(1960)].median().sort_values(ascending=False).index\n",
    "\n",
    "# Add \"world\" to the list of continents\n",
    "median_values = [\"World\"] + list(median_values)\n",
    "\n",
    "# Create a new DataFrame that includes data for all countries (world)\n",
    "df_world = df_rural.copy()\n",
    "df_world[\"Continent\"] = \"World\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e2696c9b-8624-4799-b9bf-acbb9f9bf775",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the layout of the dashboard for init\n",
    "app.layout = html.Div(\n",
    "    id='app-container',\n",
    "    style={\n",
    "        \"display\": \"flex\",\n",
    "        \"flex-direction\": \"column\",\n",
    "        \"align-items\": \"center\",\n",
    "        \"justify-content\": \"space-between\",\n",
    "        \"backgroundColor\": \"white\",\n",
    "        \"padding\": \"5px\",\n",
    "        \"margin\": \"0\",\n",
    "        \"width\": \"100%\",\n",
    "        \"height\": \"99vh\",\n",
    "        \"overflow-y\": \"hidden\",\n",
    "        \"box-sizing\": \"border-box\",\n",
    "        \"font-family\": \"Arial\",\n",
    "    },\n",
    "    children=[\n",
    "        ## Hidden Divs\n",
    "        html.Div(id='selected-year', style={'display': 'none'}),\n",
    "        html.Div(id='hovered-country', style={'display': 'none'}),\n",
    "        ######### App content #########\n",
    "        # Contains the title and some infoSS\n",
    "        html.Div(\n",
    "            id='header-container',\n",
    "            style={\n",
    "                \"backgroundColor\": \"black\",\n",
    "                \"padding\": \"10px\",\n",
    "                \"width\": \"100%\",\n",
    "                \"height\": \"8%\",\n",
    "                \"box-sizing\": \"border-box\",\n",
    "                \"display\": \"flex\",\n",
    "                \"flex-direction\": \"row\",\n",
    "                \"align-items\": \"center\",\n",
    "                \"justify-content\": \"center\",\n",
    "            },\n",
    "            children=[\n",
    "                html.Div(\n",
    "                    id='title-container',\n",
    "                    style={\n",
    "                        \"backgroundColor\": \"black\",\n",
    "                        \"flex\": \"2\",\n",
    "                        \"margin\": \"2px 0\",\n",
    "                    },\n",
    "                    children=[\n",
    "                        html.H1(\"Interactive Geo Data Visualization\", style={'text-align': 'center','color':'white'}),\n",
    "                        html.H4(\"WorldBank Dataset: Agriculture & Rural Development \", style={'text-align': 'center','color':'white'}),\n",
    "                    ]\n",
    "                ),\n",
    "                html.Div(\n",
    "                    id='info-container',\n",
    "                    style={\n",
    "                        \"backgroundColor\": \"black\",\n",
    "                        \"flex\": \"1\",\n",
    "                        \"flex-direction\":\"column\",\n",
    "                        \"justify-content\":\"center\",\n",
    "                        \"align-items\":\"center\",\n",
    "                        \"font-weight\": \"bold\",\n",
    "                        \"font-size\":\"12px\",\n",
    "                    },\n",
    "                    children=[\n",
    "                        html.P(\"Animation playback speed can be adjusted, slow connection or pc it is advised to use above 500ms refresh rate.\", style={'text-align': 'right','color':'white'}),\n",
    "                        html.P(\"Hovering over the boxplot will show the countries of a continent. Hover over World to see all countries again.\", style={'text-align': 'right','color':'white'}),\n",
    "                        html.P(\"Hovering over the choropleth will show the country in the barplot and highlight it's position in the histogram.\", style={'text-align': 'right','color':'white'}),\n",
    "                    ]\n",
    "                ),\n",
    "            ]\n",
    "        ),\n",
    "        # End of title and info container\n",
    "\n",
    "        # Contains the graphs\n",
    "        html.Div(\n",
    "            id='content-container',\n",
    "            style={\n",
    "                \"backgroundColor\": \"white\",\n",
    "                \"padding\": \"5px\",\n",
    "                \"width\": \"100%\",\n",
    "                \"height\": \"100%\",\n",
    "                \"box-sizing\": \"border-box\",\n",
    "                \"display\": \"flex\",\n",
    "                \"flex-direction\": \"column\",\n",
    "                \"justify-content\": \"space-between\",\n",
    "            },\n",
    "            children=[\n",
    "                html.Div(\n",
    "                    id='top-container',\n",
    "                    style={\n",
    "                        \"backgroundColor\": \"white\",\n",
    "                        \"flex\": \"6\",\n",
    "                        \"display\": \"flex\",\n",
    "                        \"flex-direction\": \"row\",\n",
    "                        \"justify-content\": \"space-between\",\n",
    "                    },\n",
    "                    children=[\n",
    "                        html.Div(\n",
    "                            id='left-graph',\n",
    "                            style={\n",
    "                                \"backgroundColor\": \"white\",\n",
    "                                \"flex\": \"3\",\n",
    "                                \"padding\": \"5px\",\n",
    "                                \"border\": \"1px solid black\",\n",
    "                            },\n",
    "                            children=[\n",
    "                                dcc.Graph(\n",
    "                                id='chloropleth-map',\n",
    "                                figure=px.choropleth(\n",
    "                                    df_rural,\n",
    "                                    locations='Country Code',\n",
    "                                    color=str(min_year),  # Initialize with the minimum year\n",
    "                                    hover_name='Country Name',\n",
    "                                    hover_data=['Continent','Country Code'],\n",
    "                                    title=f'Choropleth Map of Rural Percentage ({str(min_year)})',\n",
    "                                    projection='equirectangular',\n",
    "                                    color_continuous_scale='Viridis',\n",
    "                                    color_continuous_midpoint=50,\n",
    "                                    range_color=[0, 100],\n",
    "                                ).update_layout(\n",
    "                                    title={'x': 0.5},\n",
    "                                    margin={'l': 0, 'r': 0, 't': 50, 'b': 0},\n",
    "                                    hovermode='closest',\n",
    "                                    dragmode=False\n",
    "                                ),\n",
    "                                    # Chloropleth Style\n",
    "                                    style={'flex': '1', 'width': '100%','height':'100%'}\n",
    "                                )\n",
    "                            ]\n",
    "                        ),\n",
    "                        html.Div(\n",
    "                            id='right-graph',\n",
    "                            style={\n",
    "                                \"backgroundColor\": \"white\",\n",
    "                                \"flex\": \"1\",\n",
    "                                \"padding\": \"5px\",\n",
    "                                \"border\": \"1px solid black\",\n",
    "                            },\n",
    "                            children=[\n",
    "                                dcc.Graph(\n",
    "                                    id='barplot',\n",
    "                                    figure=px.bar(\n",
    "                                        df_rural[df_rural['Country Name'] == 'China'] \\\n",
    "                                            .melt(\n",
    "                                                id_vars=['Country Name', 'Country Code', 'Continent', \\\n",
    "                                                         'Indicator Name', 'Indicator Code'], \n",
    "                                                var_name='Year', \n",
    "                                                value_name='Rural population Pcnt.'),\n",
    "                                        x='Rural population Pcnt.', \n",
    "                                        y='Year', \n",
    "                                        hover_data=['Country Code', 'Country Name','Rural population Pcnt.','Year'],\n",
    "                                        orientation='h',\n",
    "                                        color='Rural population Pcnt.',\n",
    "                                        color_continuous_scale='Bluered_r',\n",
    "                                        color_continuous_midpoint=50,\n",
    "                                        range_color=[0, 100],\n",
    "                                        title=f'Hover-over: Rural Population (% of Total Population) in {\"CHN\"}',\n",
    "                                    ).update_layout(\n",
    "                                        dragmode=False,\n",
    "                                        title={'x': 0.5},\n",
    "                                        showlegend=False,\n",
    "                                    ),\n",
    "                                    # Bar Style\n",
    "                                    style={'flex': '1', 'width': '100%','height':'100%'}\n",
    "                                )\n",
    "                            ]\n",
    "                        ),\n",
    "                    ]\n",
    "                ),\n",
    "                html.Div(\n",
    "                    id='bottom-container',\n",
    "                    style={\n",
    "                        \"backgroundColor\": \"white\",\n",
    "                        \"flex\": \"4\",\n",
    "                        \"display\": \"flex\",\n",
    "                        \"flex-direction\": \"row\",\n",
    "                        \"justify-content\": \"space-between\",\n",
    "                    },\n",
    "                    children=[\n",
    "                        html.Div(\n",
    "                            id='bottom-left-graph',\n",
    "                            style={\n",
    "                                \"backgroundColor\": \"white\",\n",
    "                                \"flex\": \"1\",\n",
    "                                \"padding\": \"5px\",\n",
    "                                \"border\": \"1px solid black\",\n",
    "                            },\n",
    "                            children=[\n",
    "                                dcc.Graph(\n",
    "                                    id='box',\n",
    "                                    figure=px.box(\n",
    "                                        pd.concat([df_world,df_rural]),\n",
    "                                        x=\"Continent\", \n",
    "                                        y=str(1960),\n",
    "                                        color='Continent',\n",
    "                                        category_orders={\"Continent\": median_values},  # Order by descending medians\n",
    "                                        title=f'Box Plot of Rural Percentage (1960)',\n",
    "                                    ).update_layout(\n",
    "                                        title={'x': 0.5},\n",
    "                                        dragmode=False,\n",
    "                                    ).update_traces(\n",
    "                                        showlegend=False\n",
    "                                    ).update_yaxes(\n",
    "                                        title=\"Population % Living in Rural Areas\",\n",
    "                                        range=[0, 100],\n",
    "                                    ).update_xaxes(\n",
    "                                        title=\"Grouped by Continent\",\n",
    "                                    ),\n",
    "                                    # Box style\n",
    "                                    style={'flex': '1', 'width': '100%','height':'100%'}\n",
    "                                )\n",
    "                            ]\n",
    "                        ),\n",
    "                        html.Div(\n",
    "                            id='bottom-right-graph',\n",
    "                            style={\n",
    "                                \"backgroundColor\": \"white\",\n",
    "                                \"flex\": \"1\",\n",
    "                                \"padding\": \"5px\",\n",
    "                                \"border\": \"1px solid black\",\n",
    "                            },\n",
    "                            children=[\n",
    "                                dcc.Graph(\n",
    "                                    id='histogram',\n",
    "                                    figure=go.Figure(\n",
    "                                        data=[\n",
    "                                            go.Histogram(\n",
    "                                                x=df_rural[str(1960)],\n",
    "                                                nbinsx=20,\n",
    "                                                xbins=dict(\n",
    "                                                    start=0,\n",
    "                                                    end=100,\n",
    "                                                    size=5  # Set the bin size to 5 to create 5% intervals\n",
    "                                                ),\n",
    "                                                name='All Countries',\n",
    "                                                marker=dict(color='lightgray', line=dict(width=2)),  # Change the color and line properties as needed\n",
    "                                                opacity=0.7,\n",
    "                                            ),\n",
    "                                        ],\n",
    "                                        layout=dict(\n",
    "                                            title=f'Histogram Bins of Rural Percentage Globally (1960)',\n",
    "                                            xaxis_title=\"Percentage Range Bins\",\n",
    "                                            yaxis_title=\"Number of Countries\",\n",
    "                                            yaxis=dict(range=[0, 30]),  # Set the y-axis range to [0, 30]\n",
    "                                            xaxis=dict(range=[0, 100],dtick=5),  # Set the x-axis range to [0, 100] and dtick to 5\n",
    "                                            dragmode=False,\n",
    "                                        )\n",
    "                                    ),\n",
    "                                    style={'flex': '1', 'width': '100%', 'height': '100%'}\n",
    "                                )\n",
    "                            ]\n",
    "                        ),\n",
    "\n",
    "                    ]\n",
    "                ),\n",
    "            ]\n",
    "        ),\n",
    "        # End of graph container\n",
    "\n",
    "        # Contains the play controlls\n",
    "        html.Div(\n",
    "            id='footer-container',\n",
    "            style={\n",
    "                \"backgroundColor\": \"black\",\n",
    "                \"padding\": \"10px 0\",\n",
    "                \"width\": \"100%\",\n",
    "                \"display\": \"flex\",\n",
    "                \"flex-direction\": \"row\",\n",
    "                \"justify-content\": \"space-between\",\n",
    "                \"borderRadius\": \"5px\",\n",
    "            },\n",
    "            children=[\n",
    "                html.Div(\n",
    "                    id='footer-child-1',\n",
    "                    style={\n",
    "                        \"backgroundColor\": \"black\",\n",
    "                        \"flex\": \"none\", \n",
    "                        \"width\": \"100px\",\n",
    "                        \"margin\": \"0 10px\",\n",
    "                    },\n",
    "                    children=[\n",
    "                        # Play/pause button\n",
    "                        html.Button(\n",
    "                            \"Play\",\n",
    "                            id=\"play-button\",\n",
    "                            n_clicks=0,\n",
    "                            style={'flex': '1', 'width': '100%','height':'100%'}\n",
    "                        ),\n",
    "                    ]\n",
    "                ),\n",
    "                html.Div(\n",
    "                    id='footer-child-2',\n",
    "                    style={\n",
    "                        \"backgroundColor\": \"black\",\n",
    "                        \"flex\": \"8\",\n",
    "                        \"margin\": \"0 10px\", \n",
    "                        \"fontWeight\": \"bold\",\n",
    "                        \"borderRadius\": \"5px\",\n",
    "                    },\n",
    "                    children=[\n",
    "                        # Slider for selecting years\n",
    "                        dcc.Slider(\n",
    "                            id='year-slider',\n",
    "                            min=min_year,\n",
    "                            max=max_year,\n",
    "                            step=1,\n",
    "                            value=min_year,\n",
    "                            marks={str(year): str(year) for year in years},\n",
    "                            included=False,\n",
    "                        ),\n",
    "                        # Interval component for animation\n",
    "                        dcc.Interval(\n",
    "                            id='animation-interval',\n",
    "                            interval=300,  # Interval in milliseconds\n",
    "                            disabled=True,  # Initially disabled\n",
    "                        ),\n",
    "                        html.Div(\n",
    "                            style={\n",
    "                                'color':'white',\n",
    "                                'textAlign':'center',\n",
    "                            },\n",
    "                            children=[\n",
    "                            html.Label('Animation Speed (Milliseconds) - Lower is Faster'),\n",
    "                            dcc.Slider(\n",
    "                                id='speed-slider',\n",
    "                                min=50,  # Minimum interval value in milliseconds\n",
    "                                max=800,  # Maximum interval value in milliseconds\n",
    "                                step=50,  # Step size for the slider\n",
    "                                value=500,  # Initial value of the interval in milliseconds\n",
    "                            ),\n",
    "                        ]),\n",
    "                    ]\n",
    "                ),\n",
    "            ]\n",
    "        )\n",
    "        # End of play controls container\n",
    "    ##### End of App content ######\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "db3938fe-4eb3-4c2e-9f50-a63c61426b87",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; font-family: Ubuntu;}</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dash app running on http://127.0.0.1:8050/\n"
     ]
    },
    {
     "data": {
      "application/javascript": "window.open('http://127.0.0.1:8050/')",
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# In your callback function\n",
    "@app.callback(\n",
    "    Output('year-slider', 'value'),  # Output: Update the value of the year slider\n",
    "    Input('animation-interval', 'n_intervals'),  # Input: n_intervals from animation interval\n",
    "    Input('year-slider', 'value'),  # Input: Current value of the year slider\n",
    "    Input('play-button', 'n_clicks'),  # Input: Clicks on the play button\n",
    "    Input('year-slider', 'max'),  # Input: Maximum value of the year slider\n",
    "    prevent_initial_call=True  # Prevent initial call when the app starts\n",
    ")\n",
    "def update_selected_year(n_intervals, slider_value, play_button_clicks, max_year):\n",
    "    if play_button_clicks % 2 == 1:\n",
    "        # Play button is clicked, start animation\n",
    "        if slider_value < max_year:\n",
    "            slider_value += 1\n",
    "        else:\n",
    "            slider_value = min_year\n",
    "    return slider_value\n",
    "\n",
    "# Callback to update the hovered country in the hidden div\n",
    "@app.callback(\n",
    "    Output('hovered-country', 'children'),  # Output: Update the content of the hidden div for hovered country\n",
    "    Input('chloropleth-map', 'hoverData'),  # Input: Hover data from the choropleth map\n",
    "    prevent_initial_call=True  # Prevent initial call when the app starts\n",
    ")\n",
    "def update_hovered_country(hover_data):\n",
    "    if hover_data is not None and 'points' in hover_data:\n",
    "        country_name = hover_data['points'][0]['hovertext']\n",
    "        return country_name\n",
    "    else:\n",
    "        return ''\n",
    "\n",
    "# Callback to update the box hover data based on the selected continent\n",
    "@app.callback(\n",
    "    Output('box', 'hoverData'),  # Output: Update hover data for the box plot\n",
    "    Input('box', 'selectedData'),  # Input: Selected data in the box plot\n",
    "    prevent_initial_call=True  # Prevent initial call when the app starts\n",
    ")\n",
    "def update_box_hover(selected_data):\n",
    "    if selected_data is not None and 'points' in selected_data:\n",
    "        selected_continent = selected_data['points'][0]['x']\n",
    "        if selected_continent == \"World\":\n",
    "            # Return hover data for \"World\" (all countries)\n",
    "            selected_continent = None\n",
    "\n",
    "        return {'points': [{'x': selected_continent}]}\n",
    "    else:\n",
    "        return {'points': []}\n",
    "\n",
    "# Define callback functions\n",
    "@app.callback(\n",
    "    Output('chloropleth-map', 'figure'),  # Output: Update the choropleth map figure\n",
    "    Input('year-slider', 'value'),  # Input: Selected year from the year slider\n",
    "    Input('box', 'hoverData'),  # Input: Hover data from the box plot\n",
    "    prevent_initial_call=True  # Prevent initial call when the app starts\n",
    ")\n",
    "def update_choropleth_animation(selected_year, box_hover_data):\n",
    "    # Initialize the filtered DataFrame with all countries\n",
    "    filtered_df = df_rural.copy()\n",
    "\n",
    "    if box_hover_data is not None and 'points' in box_hover_data:\n",
    "        selected_continent = box_hover_data['points'][0]['x']\n",
    "        if selected_continent != \"World\":\n",
    "            # Filter the DataFrame to get data for the selected continent and year\n",
    "            filtered_df = df_rural[df_rural['Continent'] == selected_continent]\n",
    "\n",
    "    fig = px.choropleth(\n",
    "        filtered_df,\n",
    "        locations='Country Code',\n",
    "        color=str(selected_year),\n",
    "        hover_name='Country Name',\n",
    "        hover_data=['Continent', 'Country Code'],\n",
    "        title=f'Choropleth Map of Rural Percentage ({str(selected_year)})',\n",
    "        projection='equirectangular',\n",
    "        color_continuous_scale='Viridis',\n",
    "        color_continuous_midpoint=50,\n",
    "        range_color=[0, 100],\n",
    "    ).update_layout(\n",
    "        title={'x': 0.5},\n",
    "        margin={'l': 0, 'r': 0, 't': 50, 'b': 0},\n",
    "        hovermode='closest',\n",
    "        dragmode=False,\n",
    "    )\n",
    "\n",
    "    return fig\n",
    "\n",
    "@app.callback(\n",
    "    Output('barplot', 'figure'),  # Output: Update the bar plot figure\n",
    "    Input('hovered-country', 'children'),  # Input: Hovered country name\n",
    "    prevent_initial_call=True  # Prevent initial call when the app starts\n",
    ")\n",
    "def update_barplot_animation(hover_country):\n",
    "    # Update the figure with the new year\n",
    "    bar_fig = px.bar(\n",
    "        df_rural[df_rural['Country Name'] == hover_country].melt(\n",
    "            id_vars=['Country Name', 'Country Code', 'Continent', 'Indicator Name', 'Indicator Code'],\n",
    "            var_name='Year',\n",
    "            value_name='Rural population Pcnt.'\n",
    "        ),\n",
    "        x='Rural population Pcnt.',\n",
    "        y='Year',\n",
    "        hover_data=['Country Code', 'Country Name', 'Rural population Pcnt.', 'Year'],\n",
    "        orientation='h',\n",
    "        color='Rural population Pcnt.',\n",
    "        color_continuous_scale='Bluered_r',\n",
    "        color_continuous_midpoint=50,\n",
    "        range_color=[0, 100],\n",
    "        title=f'Hover-over: Rural Population (% of Total Population) in {hover_country}',\n",
    "    ).update_layout(\n",
    "        title={'x': 0.5},\n",
    "        dragmode=False,\n",
    "        showlegend=False,\n",
    "    ).update_yaxes(autorange=\"reversed\")\n",
    "\n",
    "    return bar_fig\n",
    "\n",
    "@app.callback(\n",
    "    Output('box', 'figure'),  # Output: Update the box plot figure\n",
    "    Input('year-slider', 'value'),  # Input: Selected year from the year slider\n",
    "    prevent_initial_call=True  # Prevent initial call when the app starts\n",
    ")\n",
    "def update_box_animation(selected_year):\n",
    "    # Calculate median values for each continent\n",
    "    median_values = df_rural.groupby(\"Continent\")[str(selected_year)].median().sort_values(ascending=False).index\n",
    "\n",
    "    # Add \"world\" to the list of continents\n",
    "    median_values = [\"World\"] + list(median_values)\n",
    "\n",
    "    # Create a DataFrame that includes data for all countries (world)\n",
    "    df_world = df_rural.copy()\n",
    "    df_world[\"Continent\"] = \"World\"\n",
    "\n",
    "    # Update the figure with the new year\n",
    "    box_fig = px.box(\n",
    "        pd.concat([df_rural, df_world]),  # Concatenate the data for all continents including \"world\"\n",
    "        x=\"Continent\",\n",
    "        y=str(selected_year),\n",
    "        color='Continent',\n",
    "        category_orders={\"Continent\": median_values},  # Order by descending medians\n",
    "        title=f'Scatter Plot of Rural Percentage ({str(selected_year)})',\n",
    "    ).update_layout(\n",
    "        title={'x': 0.5},\n",
    "        hovermode='closest',\n",
    "        dragmode=False\n",
    "    ).update_traces(\n",
    "        showlegend=False\n",
    "    ).update_yaxes(\n",
    "        range=[0, 100],\n",
    "        title=\"Population % Living in Rural Areas\",\n",
    "    ).update_xaxes(\n",
    "        title=\"Grouped by Continent\",\n",
    "    )\n",
    "\n",
    "    return box_fig\n",
    "\n",
    "# Callback to update the histogram based on the selected country and year slider\n",
    "@app.callback(\n",
    "    Output('histogram', 'figure'),  # Output: Update the histogram figure\n",
    "    Input('hovered-country', 'children'),  # Input: Hovered country name\n",
    "    Input('year-slider', 'value'),  # Input: Selected year from the year slider\n",
    "    Input('year-slider', 'drag_value'),  # Input: Drag value of the year slider\n",
    "    prevent_initial_call=True  # Prevent initial call when the app starts\n",
    ")\n",
    "def update_histogram_on_hover(selected_country, selected_year, drag_value):\n",
    "    # Determine whether the callback was triggered by a hover event or slider value change\n",
    "    ctx = dash.callback_context\n",
    "    triggered_component = ctx.triggered[0]['prop_id'].split('.')[0]\n",
    "\n",
    "    if triggered_component == 'year-slider':\n",
    "        # Slider value changed, update the histogram based on the selected year\n",
    "        if selected_country is None:\n",
    "            selected_country = \"All Countries\"  # Default country when slider changes\n",
    "\n",
    "    elif triggered_component == 'chloropleth-map':\n",
    "        # Hover event, use the stored selected country\n",
    "        if selected_country is None:\n",
    "            selected_country = \"All Countries\"  # Default country when no hover event\n",
    "\n",
    "    # Create a histogram trace for all countries with consistent bin settings\n",
    "    histogram_trace = go.Histogram(\n",
    "        x=df_rural[str(selected_year)],  # Update this to the appropriate year or column\n",
    "        nbinsx=20,\n",
    "        xbins=dict(\n",
    "            start=0,\n",
    "            end=100,\n",
    "            size=5  # Set the bin size to 5 to create 5% intervals\n",
    "        ),\n",
    "        name='All Countries',\n",
    "        marker=dict(line=dict(width=2)),  # Change the color and line properties as needed\n",
    "        opacity=0.7,\n",
    "    )\n",
    "\n",
    "    # Create a list of shapes to highlight the selected country's bin\n",
    "    shapes = []\n",
    "\n",
    "    if selected_country != \"All Countries\":\n",
    "        # Filter the DataFrame to get data for the selected country\n",
    "        filtered_df = df_rural[df_rural['Country Name'] == selected_country]\n",
    "\n",
    "        # Calculate the bin edges and highlight the selected country's bin\n",
    "        hist, edges = np.histogram(filtered_df[str(selected_year)], bins=20, range=[0, 100])\n",
    "        selected_bin_index = np.digitize(filtered_df.iloc[0][str(selected_year)], edges) - 1\n",
    "\n",
    "        # Calculate the center of the selected bin\n",
    "        center = (edges[selected_bin_index] + edges[selected_bin_index + 1]) / 2\n",
    "\n",
    "        # Highlight the selected bin with a different color\n",
    "        shapes.append(\n",
    "            dict(\n",
    "                type='rect',\n",
    "                x0=center - 2.5,  # Adjust the x0 and x1 to center the shape\n",
    "                x1=center + 2.5,\n",
    "                y0=0,\n",
    "                y1=max(hist),\n",
    "                xref='x',\n",
    "                yref='y',\n",
    "                fillcolor='red',  # Change the color as needed\n",
    "                opacity=1,\n",
    "                layer='above',  # Place the shape above the bars\n",
    "                line=dict(width=2, color='black'),  # Add a black outline\n",
    "            )\n",
    "        )\n",
    "\n",
    "    # Create the histogram figure with the histogram trace and shapes\n",
    "    histogram_fig = go.Figure(data=[histogram_trace], layout=dict(shapes=shapes))\n",
    "    histogram_fig.update_layout(\n",
    "        title=f'Histogram Bins of Rural Percentage Globally ({selected_year}) Hover Country: {selected_country}',\n",
    "        xaxis_title=\"Percentage Range Bins\",\n",
    "        yaxis_title=\"Number of Countries\",\n",
    "        dragmode=False,\n",
    "        yaxis=dict(range=[0, 30]),  # Set the y-axis range to [0, 30]\n",
    "        xaxis=dict(range=[0, 100],dtick=5),  # Set the x-axis range to [0, 100]\n",
    "    ).update_layout(\n",
    "        title={'x': 0.5},\n",
    "    )\n",
    "\n",
    "    return histogram_fig\n",
    "\n",
    "@app.callback(\n",
    "    Output('play-button', 'children'),  # Output: Update the play button label\n",
    "    Output('animation-interval', 'disabled'),  # Output: Enable/disable the animation interval\n",
    "    Input('play-button', 'n_clicks'),  # Input: Clicks on the play button\n",
    "    Input('animation-interval', 'disabled'),  # Input: Animation interval status (disabled/enabled)\n",
    "    prevent_initial_call=True  # Prevent initial call when the app starts\n",
    ")\n",
    "def toggle_play_pause(n_clicks, interval_disabled):\n",
    "    if n_clicks % 2 == 1:\n",
    "        return \"Pause\", False\n",
    "    else:\n",
    "        return \"Play\", True\n",
    "\n",
    "# Set animation speed\n",
    "@app.callback(\n",
    "    Output('animation-interval', 'interval'),  # Output: Update the animation interval\n",
    "    Input('speed-slider', 'value')  # Input: Speed value from the speed slider\n",
    ")\n",
    "def update_animation_interval(speed_value):\n",
    "    interval_value = speed_value\n",
    "    return interval_value\n",
    "\n",
    "# Display the app within the Jupyter Notebook cell\n",
    "display(HTML(\"<style>.container { width:100% !important; font-family: Ubuntu;}</style>\"))\n",
    "# app.run_server(debug=True, use_reloader=False)\n",
    "app.run(jupyter_mode=\"tab\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e47c0f4b-aa63-4a6f-a4de-e9ec0d5cfec1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# End of Notebook"
   ]
  }
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