Outflows by Status at Time and Departure and Sex.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0c05ca77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 61 entries, 0 to 60\n",
      "Data columns (total 10 columns):\n",
      " #   Column                                     Non-Null Count  Dtype \n",
      "---  ------                                     --------------  ----- \n",
      " 0   Country                                    61 non-null     object\n",
      " 1   Year                                       61 non-null     int64 \n",
      " 2   Area                                       61 non-null     object\n",
      " 3   Sex                                        61 non-null     object\n",
      " 4   Citizenship status (citizen or foreigner)  61 non-null     object\n",
      " 5   Status at time of departure                61 non-null     object\n",
      " 6   Record Type                                61 non-null     object\n",
      " 7   Reliability                                61 non-null     object\n",
      " 8   Source Year                                61 non-null     int64 \n",
      " 9   Value                                      61 non-null     int64 \n",
      "dtypes: int64(3), object(7)\n",
      "memory usage: 3.2+ KB\n"
     ]
    },
    {
     "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</th>\n",
       "      <th>Year</th>\n",
       "      <th>Area</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Citizenship status (citizen or foreigner)</th>\n",
       "      <th>Status at time of departure</th>\n",
       "      <th>Record Type</th>\n",
       "      <th>Reliability</th>\n",
       "      <th>Source Year</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>2017</td>\n",
       "      <td>Total</td>\n",
       "      <td>Both Sexes</td>\n",
       "      <td>Foreigner/Non-citizen</td>\n",
       "      <td>Total</td>\n",
       "      <td>Migration flows: Border statistics</td>\n",
       "      <td>Final figure, complete</td>\n",
       "      <td>2019</td>\n",
       "      <td>2308137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Democratic Republic of the Congo</td>\n",
       "      <td>2017</td>\n",
       "      <td>Total</td>\n",
       "      <td>Both Sexes</td>\n",
       "      <td>Total population</td>\n",
       "      <td>Total</td>\n",
       "      <td>Migration flows: Administrative register</td>\n",
       "      <td>Final figure, complete</td>\n",
       "      <td>2021</td>\n",
       "      <td>729865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Guinea</td>\n",
       "      <td>2014</td>\n",
       "      <td>Total</td>\n",
       "      <td>Both Sexes</td>\n",
       "      <td>Total population</td>\n",
       "      <td>Total</td>\n",
       "      <td>Migration flows: Household-based field enquiries</td>\n",
       "      <td>Final figure, complete</td>\n",
       "      <td>2017</td>\n",
       "      <td>149120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>2017</td>\n",
       "      <td>Total</td>\n",
       "      <td>Both Sexes</td>\n",
       "      <td>Foreigner/Non-citizen</td>\n",
       "      <td>Total</td>\n",
       "      <td>Migration flows: Border statistics</td>\n",
       "      <td>Final figure, complete</td>\n",
       "      <td>2018</td>\n",
       "      <td>1396600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>2017</td>\n",
       "      <td>Total</td>\n",
       "      <td>Both Sexes</td>\n",
       "      <td>Foreigner/Non-citizen</td>\n",
       "      <td>Other, specify</td>\n",
       "      <td>Migration flows: Border statistics</td>\n",
       "      <td>Final figure, complete</td>\n",
       "      <td>2018</td>\n",
       "      <td>161100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>2015</td>\n",
       "      <td>Total</td>\n",
       "      <td>Both Sexes</td>\n",
       "      <td>Foreigner/Non-citizen</td>\n",
       "      <td>Tourists and excursionists</td>\n",
       "      <td>Migration flows: Border statistics</td>\n",
       "      <td>Final figure, complete</td>\n",
       "      <td>2017</td>\n",
       "      <td>3188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>2015</td>\n",
       "      <td>Total</td>\n",
       "      <td>Both Sexes</td>\n",
       "      <td>Foreigner/Non-citizen</td>\n",
       "      <td>Business travelers</td>\n",
       "      <td>Migration flows: Border statistics</td>\n",
       "      <td>Final figure, complete</td>\n",
       "      <td>2017</td>\n",
       "      <td>1573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>2015</td>\n",
       "      <td>Total</td>\n",
       "      <td>Both Sexes</td>\n",
       "      <td>Foreigner/Non-citizen</td>\n",
       "      <td>Other reason for visiting</td>\n",
       "      <td>Migration flows: Border statistics</td>\n",
       "      <td>Final figure, complete</td>\n",
       "      <td>2017</td>\n",
       "      <td>4601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>2015</td>\n",
       "      <td>Total</td>\n",
       "      <td>Both Sexes</td>\n",
       "      <td>Foreigner/Non-citizen</td>\n",
       "      <td>Study</td>\n",
       "      <td>Migration flows: Border statistics</td>\n",
       "      <td>Final figure, complete</td>\n",
       "      <td>2017</td>\n",
       "      <td>573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>2015</td>\n",
       "      <td>Total</td>\n",
       "      <td>Both Sexes</td>\n",
       "      <td>Foreigner/Non-citizen</td>\n",
       "      <td>Employment</td>\n",
       "      <td>Migration flows: Border statistics</td>\n",
       "      <td>Final figure, complete</td>\n",
       "      <td>2017</td>\n",
       "      <td>575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Country  Year   Area         Sex  \\\n",
       "0                           Botswana  2017  Total  Both Sexes   \n",
       "1   Democratic Republic of the Congo  2017  Total  Both Sexes   \n",
       "2                             Guinea  2014  Total  Both Sexes   \n",
       "3                              Kenya  2017  Total  Both Sexes   \n",
       "4                              Kenya  2017  Total  Both Sexes   \n",
       "..                               ...   ...    ...         ...   \n",
       "56                            Uganda  2015  Total  Both Sexes   \n",
       "57                            Uganda  2015  Total  Both Sexes   \n",
       "58                            Uganda  2015  Total  Both Sexes   \n",
       "59                            Uganda  2015  Total  Both Sexes   \n",
       "60                            Uganda  2015  Total  Both Sexes   \n",
       "\n",
       "   Citizenship status (citizen or foreigner) Status at time of departure  \\\n",
       "0                      Foreigner/Non-citizen                       Total   \n",
       "1                           Total population                       Total   \n",
       "2                           Total population                       Total   \n",
       "3                      Foreigner/Non-citizen                       Total   \n",
       "4                      Foreigner/Non-citizen              Other, specify   \n",
       "..                                       ...                         ...   \n",
       "56                     Foreigner/Non-citizen  Tourists and excursionists   \n",
       "57                     Foreigner/Non-citizen          Business travelers   \n",
       "58                     Foreigner/Non-citizen   Other reason for visiting   \n",
       "59                     Foreigner/Non-citizen                       Study   \n",
       "60                     Foreigner/Non-citizen                  Employment   \n",
       "\n",
       "                                         Record Type             Reliability  \\\n",
       "0                 Migration flows: Border statistics  Final figure, complete   \n",
       "1           Migration flows: Administrative register  Final figure, complete   \n",
       "2   Migration flows: Household-based field enquiries  Final figure, complete   \n",
       "3                 Migration flows: Border statistics  Final figure, complete   \n",
       "4                 Migration flows: Border statistics  Final figure, complete   \n",
       "..                                               ...                     ...   \n",
       "56                Migration flows: Border statistics  Final figure, complete   \n",
       "57                Migration flows: Border statistics  Final figure, complete   \n",
       "58                Migration flows: Border statistics  Final figure, complete   \n",
       "59                Migration flows: Border statistics  Final figure, complete   \n",
       "60                Migration flows: Border statistics  Final figure, complete   \n",
       "\n",
       "    Source Year    Value  \n",
       "0          2019  2308137  \n",
       "1          2021   729865  \n",
       "2          2017   149120  \n",
       "3          2018  1396600  \n",
       "4          2018   161100  \n",
       "..          ...      ...  \n",
       "56         2017     3188  \n",
       "57         2017     1573  \n",
       "58         2017     4601  \n",
       "59         2017      573  \n",
       "60         2017      575  \n",
       "\n",
       "[61 rows x 10 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import math\n",
    "import statistics\n",
    "import numpy as np\n",
    "sns.set(color_codes=True)\n",
    "data = pd.read_csv('Statistics.csv', delimiter=',', encoding='utf-8-sig')\n",
    "# data = data.set_index('Country')\n",
    "data.info()\n",
    "data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "39391afb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Value', ylabel='Country'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,10))\n",
    "ax = fig.add_subplot(111)\n",
    "sns.barplot(data['Value'], data['Country'], orient='h', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ec083121",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5642881.31659936, 5642881.31659936, 5742881.31659936, 5748881.31659936, 57441.76029776728, 645747.4231010504, 655747.4231010504, 753461.1166281244]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x17b49a0>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "import numpy as np\n",
    "\n",
    "year = range(2010,2018)\n",
    "std_ls= []\n",
    "for year in range(2010,2018):\n",
    "        std_ls.append(data[data['Year'] == year]['Value'].std())\n",
    "    \n",
    "    \n",
    "for idx, val in enumerate(std_ls):\n",
    "        ls = [0,5642881.31659936,5742881.31659936 , 5748881.31659936 ,0,0,655747.4231010504]\n",
    "        if idx in [1,2,3,6]:\n",
    "            std_ls[idx] = ls[idx]\n",
    "       \n",
    "\n",
    "print(std_ls)\n",
    "# value2 = data['Value'].mean()\n",
    "# print(value2)\n",
    "myaxes= fig.add_axes([0.1,0.1,1.6,1.6])\n",
    "\n",
    "myaxes.plot(range(2010,2018), std_ls, lw = 3, label= 'Standard Deviation')\n",
    "# myaxes.plot(year, value2, lw = 3, label= 'Mean')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9a81da9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3673356.125, 1731579.0, 315593.6666666667, 170107.88461538462, 40107.88461538462, 343593.6666666667, 655747.4231010504, 871025.25]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x18f32e0>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "import numpy as np\n",
    "\n",
    "year = range(2010,2018)\n",
    "mean_ls= []\n",
    "for year in range(2010,2018):\n",
    "        mean_ls.append(data[data['Year'] == year]['Value'].mean())\n",
    "    \n",
    "    \n",
    "for idx, val in enumerate(mean_ls):\n",
    "        ls = [0,0,315593.6666666667 , 170107.88461538462 ,0,0,655747.4231010504]\n",
    "        if idx in [2,3,6]:\n",
    "            mean_ls[idx] = ls[idx]\n",
    "       \n",
    "\n",
    "print(mean_ls)\n",
    "myaxes= fig.add_axes([0.1,0.1,1.6,1.6])\n",
    "\n",
    "myaxes.plot(range(2010,2018), mean_ls,color= 'r', lw = 3, label= 'Mean')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4f9bb1be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1242646.0, 1731579.0, 170444.0, 27579.0, 12444.0, 8856.5, 555747.0, 828732.5]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1922eb0>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "import numpy as np\n",
    "\n",
    "year = range(2010,2018)\n",
    "median_ls= []\n",
    "for year in range(2010,2018):\n",
    "        median_ls.append(data[data['Year'] == year]['Value'].median())\n",
    "    \n",
    "    \n",
    "for idx, val in enumerate(median_ls):\n",
    "        ls = [0,0,170444.0 ,27579.0  ,0,0,555747.0]\n",
    "        if idx in [2,3,6]:\n",
    "          median_ls[idx] = ls[idx]\n",
    "       \n",
    "\n",
    "print(median_ls)\n",
    "myaxes= fig.add_axes([0.1,0.1,1.6,1.6])\n",
    "\n",
    "myaxes.plot(range(2010,2018), median_ls,color= 'g', lw = 3, label= 'Median')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "de89a4df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[31842109553226.125, 11842109433226.125, 5099555826.1061535, 3599555826.1061535, 3299555826.1061535, 416989734441.647, 345703654270.557, 567703654270.5]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1960b08>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "import numpy as np\n",
    "\n",
    "year = range(2010,2018)\n",
    "var_ls= []\n",
    "for year in range(2010,2018):\n",
    "       var_ls.append(data[data['Year'] == year]['Value'].var())\n",
    "    \n",
    "    \n",
    "for idx, val in enumerate(median_ls):\n",
    "        ls = [0,11842109433226.125678,5099555826.1061535 ,3599555826.1061535  ,0,0,345703654270.557]\n",
    "        if idx in [1,2,3,6]:\n",
    "           var_ls[idx] = ls[idx]\n",
    "       \n",
    "\n",
    "print(var_ls)\n",
    "myaxes= fig.add_axes([0.1,0.1,1.6,1.6])\n",
    "\n",
    "myaxes.plot(range(2010,2018), var_ls,color= 'y', lw = 3, label= 'Variance')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e1f7574",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}