brazilian_population_growth_visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d9f7c68e",
   "metadata": {},
   "source": [
    "# Data Visualization with Python\n",
    "\n",
    "**Objectives**\n",
    "\n",
    "* Visualize Data with Python\n",
    "* Get to know the MATPLOTLIB PYPLOT library\n",
    "* Build line, bar, scatter and boxplot charts\n",
    "* Manipulate data to build graphs\n",
    "\n",
    "**Dataset features**\n",
    "\n",
    "Brazilian population growth from 1980 to 2016\n",
    "\n",
    "* Rows: 37\n",
    "* Columns: 2\n",
    "* File format: csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5505a65",
   "metadata": {},
   "source": [
    "### Implementing in Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bf7ee939",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# DataSus\n",
    "data = open(\"brazilian_population.csv\").readlines()\n",
    "\n",
    "x = []\n",
    "y = []\n",
    "\n",
    "for i in range(len(data)):\n",
    "    if i != 0: # Skip the feature name row\n",
    "        line = data[i].split(\";\")\n",
    "        x.append(int(line[0])) # Year\n",
    "        y.append(int(line[1])) # Population\n",
    "        \n",
    "plt.bar(x, y, color=\"#e4e4e4\")\n",
    "plt.plot(x, y, color=\"k\", linestyle=\"--\")\n",
    "plt.title(\"Brazilian population growth 1980 - 2016\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Population x100,000,000\")\n",
    "plt.show()\n",
    "# To use in Python\n",
    "plt.savefig(\"populacao_brasileira.png\", dpi = 300) # Recommended resolution for printing\n",
    "plt.savefig(\"populacao_brasileira.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee772091",
   "metadata": {},
   "source": [
    "### Example of boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "058230f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x1a24923fa90>,\n",
       "  <matplotlib.lines.Line2D at 0x1a24923fd60>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x1a249253130>,\n",
       "  <matplotlib.lines.Line2D at 0x1a249253340>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x1a24923f7c0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x1a249253610>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x1a2492538e0>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(y)\n",
    "#plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63c90fcb",
   "metadata": {},
   "source": [
    "# Implementing using Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f1d11df9",
   "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>ano</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>119011052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>121154159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>123774229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>126403352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>129025577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1985</td>\n",
       "      <td>131639272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1986</td>\n",
       "      <td>134228492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1987</td>\n",
       "      <td>136780739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1988</td>\n",
       "      <td>139280140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1989</td>\n",
       "      <td>141714953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1990</td>\n",
       "      <td>144090756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1991</td>\n",
       "      <td>146825475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1992</td>\n",
       "      <td>148684120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1993</td>\n",
       "      <td>151556521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1994</td>\n",
       "      <td>153726463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1995</td>\n",
       "      <td>155822296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1996</td>\n",
       "      <td>157070163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1997</td>\n",
       "      <td>159636297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1998</td>\n",
       "      <td>161790182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1999</td>\n",
       "      <td>163947436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2000</td>\n",
       "      <td>169799170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2001</td>\n",
       "      <td>172385776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2002</td>\n",
       "      <td>174632932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2003</td>\n",
       "      <td>176876251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2004</td>\n",
       "      <td>179108134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2005</td>\n",
       "      <td>184184074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2006</td>\n",
       "      <td>186770613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2007</td>\n",
       "      <td>189335191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2008</td>\n",
       "      <td>189612814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2009</td>\n",
       "      <td>191481045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2010</td>\n",
       "      <td>190755799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2011</td>\n",
       "      <td>192379287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2012</td>\n",
       "      <td>193976530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2013</td>\n",
       "      <td>201062789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2014</td>\n",
       "      <td>202799518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2015</td>\n",
       "      <td>204482459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2016</td>\n",
       "      <td>206114067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ano  population\n",
       "0   1980   119011052\n",
       "1   1981   121154159\n",
       "2   1982   123774229\n",
       "3   1983   126403352\n",
       "4   1984   129025577\n",
       "5   1985   131639272\n",
       "6   1986   134228492\n",
       "7   1987   136780739\n",
       "8   1988   139280140\n",
       "9   1989   141714953\n",
       "10  1990   144090756\n",
       "11  1991   146825475\n",
       "12  1992   148684120\n",
       "13  1993   151556521\n",
       "14  1994   153726463\n",
       "15  1995   155822296\n",
       "16  1996   157070163\n",
       "17  1997   159636297\n",
       "18  1998   161790182\n",
       "19  1999   163947436\n",
       "20  2000   169799170\n",
       "21  2001   172385776\n",
       "22  2002   174632932\n",
       "23  2003   176876251\n",
       "24  2004   179108134\n",
       "25  2005   184184074\n",
       "26  2006   186770613\n",
       "27  2007   189335191\n",
       "28  2008   189612814\n",
       "29  2009   191481045\n",
       "30  2010   190755799\n",
       "31  2011   192379287\n",
       "32  2012   193976530\n",
       "33  2013   201062789\n",
       "34  2014   202799518\n",
       "35  2015   204482459\n",
       "36  2016   206114067"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"brazilian_population.csv\", sep=\";\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "38ca9e4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example of different marker\n",
    "plt.plot(df['ano'], df['population'], color=\"m\", linestyle=\":\")\n",
    "# s can vary with the size of vector elements\n",
    "plt.scatter(df['ano'], df['population'], color=\"c\", marker=\"|\", s=(df['population']/1000000)) \n",
    "\n",
    "plt.title(\"Brazilian population growth 1980 - 2016\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Population x100,000,000\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6ba2f6d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting to array and spliting\n",
    "x = df['ano'].to_numpy()\n",
    "y = df['population'].to_numpy()\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a24f6a6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression()\n",
    "# Converting one row to many rows corresponding to the number of elements from the array\n",
    "model.fit(x_train.reshape(-1, 1), y_train.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d01ad566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predicting and ploting\n",
    "y_pred = model.predict(x_test.reshape(-1, 1))\n",
    "\n",
    "plt.plot(range(y_pred.shape[0]), y_pred,'r--')\n",
    "plt.plot(range(y_test.shape[0]), y_test,'g--')\n",
    "plt.legend(['predicted', 'actual'])\n",
    "plt.ylabel('Population')\n",
    "plt.xlabel('Index')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b57c5408",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2-score:  0.9979749227681759\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "print('R2-score: ', r2_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e9deb6a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Trying to predict new years\n",
    "new_years = np.array([2017, 2018, 2019, 2020, 2021, 2022])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b3d2dd5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Year Population\n",
      "0  2017  210094546\n",
      "1  2018  212546911\n",
      "2  2019  214999277\n",
      "3  2020  217451642\n",
      "4  2021  219904007\n",
      "5  2022  222356373\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predicting from 2017 to 2022\n",
    "growth_pred = model.predict(new_years.reshape(-1, 1))\n",
    "\n",
    "# flatten to convert multidimensional to on dimensional\n",
    "df2 = pd.DataFrame({'Year': new_years, 'Population': growth_pred.flatten()})\n",
    "\n",
    "# map function to convert float in scientific notation to string. Eg. 2.231693e+08 -> 223169287\n",
    "df2['Population'] = df2['Population'].map(lambda x: '{:.0f}'.format(x))\n",
    "print(df2)\n",
    "\n",
    "plt.plot(new_years, growth_pred.flatten(),'r--')\n",
    "plt.grid()\n",
    "plt.title(\"Brazilian population growth 2017 - 2022 predict\")\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel(\"Population x100,000,000\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.bar(new_years, growth_pred.flatten(), color=\"k\")\n",
    "plt.bar(x, y, color=\"#e4e4e4\")\n",
    "plt.title(\"Brazilian population growth 1980 - 2022 predict\")\n",
    "plt.legend(['previsto', 'real'])\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel(\"Population x100,000,000\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ad600ab9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The last, 2022 is a prediction from IBGE\n",
    "actual_new_growth = np.array([207800000, 209500000, 211000000, 212600000, 213300000, 214800000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dc204c29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(new_years, growth_pred,'r--')\n",
    "plt.plot(new_years, actual_new_growth,'g--')\n",
    "plt.grid()\n",
    "plt.title(\"Brazilian population growth 2017 - 2022 predict\")\n",
    "plt.legend(['previsto', 'real'])\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel(\"Population x100,000,000\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57e79aeb",
   "metadata": {},
   "source": [
    "Of course, I am ignoring several features such as the pandemic factor in the population calculation.\n",
    "But I will try a better model later."
   ]
  },
  {
   "attachments": {
    "20210825-210825a-estimativa-da-populacao-brasileira-2010-2022.jpg": {
     "image/jpeg": 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"
    }
   },
   "cell_type": "markdown",
   "id": "31f5416b",
   "metadata": {},
   "source": [
    "![20210825-210825a-estimativa-da-populacao-brasileira-2010-2022.jpg](attachment:20210825-210825a-estimativa-da-populacao-brasileira-2010-2022.jpg)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}