notebooks/01-conhecendo_a_bibliteca_matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Region</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>47</td>\n",
       "      <td>71</td>\n",
       "      <td>340</td>\n",
       "      <td>...</td>\n",
       "      <td>3436</td>\n",
       "      <td>3009</td>\n",
       "      <td>2652</td>\n",
       "      <td>2111</td>\n",
       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "      <td>58639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1223</td>\n",
       "      <td>856</td>\n",
       "      <td>702</td>\n",
       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "      <td>15699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>67</td>\n",
       "      <td>71</td>\n",
       "      <td>69</td>\n",
       "      <td>63</td>\n",
       "      <td>44</td>\n",
       "      <td>...</td>\n",
       "      <td>3626</td>\n",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "      <td>69439</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country Continent           Region             DevName  1980  1981  \\\n",
       "0  Afghanistan      Asia    Southern Asia  Developing regions    16    39   \n",
       "1      Albania    Europe  Southern Europe   Developed regions     1     0   \n",
       "2      Algeria    Africa  Northern Africa  Developing regions    80    67   \n",
       "\n",
       "   1982  1983  1984  1985  ...  2005  2006  2007  2008  2009  2010  2011  \\\n",
       "0    39    47    71   340  ...  3436  3009  2652  2111  1746  1758  2203   \n",
       "1     0     0     0     0  ...  1223   856   702   560   716   561   539   \n",
       "2    71    69    63    44  ...  3626  4807  3623  4005  5393  4752  4325   \n",
       "\n",
       "   2012  2013  Total  \n",
       "0  2635  2004  58639  \n",
       "1   620   603  15699  \n",
       "2  3774  4331  69439  \n",
       "\n",
       "[3 rows x 39 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../dados/canadian_immegration_data.csv')\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 195 entries, 0 to 194\n",
      "Data columns (total 39 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   Country    195 non-null    object\n",
      " 1   Continent  195 non-null    object\n",
      " 2   Region     195 non-null    object\n",
      " 3   DevName    195 non-null    object\n",
      " 4   1980       195 non-null    int64 \n",
      " 5   1981       195 non-null    int64 \n",
      " 6   1982       195 non-null    int64 \n",
      " 7   1983       195 non-null    int64 \n",
      " 8   1984       195 non-null    int64 \n",
      " 9   1985       195 non-null    int64 \n",
      " 10  1986       195 non-null    int64 \n",
      " 11  1987       195 non-null    int64 \n",
      " 12  1988       195 non-null    int64 \n",
      " 13  1989       195 non-null    int64 \n",
      " 14  1990       195 non-null    int64 \n",
      " 15  1991       195 non-null    int64 \n",
      " 16  1992       195 non-null    int64 \n",
      " 17  1993       195 non-null    int64 \n",
      " 18  1994       195 non-null    int64 \n",
      " 19  1995       195 non-null    int64 \n",
      " 20  1996       195 non-null    int64 \n",
      " 21  1997       195 non-null    int64 \n",
      " 22  1998       195 non-null    int64 \n",
      " 23  1999       195 non-null    int64 \n",
      " 24  2000       195 non-null    int64 \n",
      " 25  2001       195 non-null    int64 \n",
      " 26  2002       195 non-null    int64 \n",
      " 27  2003       195 non-null    int64 \n",
      " 28  2004       195 non-null    int64 \n",
      " 29  2005       195 non-null    int64 \n",
      " 30  2006       195 non-null    int64 \n",
      " 31  2007       195 non-null    int64 \n",
      " 32  2008       195 non-null    int64 \n",
      " 33  2009       195 non-null    int64 \n",
      " 34  2010       195 non-null    int64 \n",
      " 35  2011       195 non-null    int64 \n",
      " 36  2012       195 non-null    int64 \n",
      " 37  2013       195 non-null    int64 \n",
      " 38  Total      195 non-null    int64 \n",
      "dtypes: int64(35), object(4)\n",
      "memory usage: 59.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Continent</th>\n",
       "      <th>Region</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>47</td>\n",
       "      <td>71</td>\n",
       "      <td>340</td>\n",
       "      <td>496</td>\n",
       "      <td>...</td>\n",
       "      <td>3436</td>\n",
       "      <td>3009</td>\n",
       "      <td>2652</td>\n",
       "      <td>2111</td>\n",
       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "      <td>58639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1223</td>\n",
       "      <td>856</td>\n",
       "      <td>702</td>\n",
       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "      <td>15699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>67</td>\n",
       "      <td>71</td>\n",
       "      <td>69</td>\n",
       "      <td>63</td>\n",
       "      <td>44</td>\n",
       "      <td>69</td>\n",
       "      <td>...</td>\n",
       "      <td>3626</td>\n",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "      <td>69439</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Continent           Region             DevName  1980  1981  1982  \\\n",
       "Country                                                                        \n",
       "Afghanistan      Asia    Southern Asia  Developing regions    16    39    39   \n",
       "Albania        Europe  Southern Europe   Developed regions     1     0     0   \n",
       "Algeria        Africa  Northern Africa  Developing regions    80    67    71   \n",
       "\n",
       "             1983  1984  1985  1986  ...  2005  2006  2007  2008  2009  2010  \\\n",
       "Country                              ...                                       \n",
       "Afghanistan    47    71   340   496  ...  3436  3009  2652  2111  1746  1758   \n",
       "Albania         0     0     0     1  ...  1223   856   702   560   716   561   \n",
       "Algeria        69    63    44    69  ...  3626  4807  3623  4005  5393  4752   \n",
       "\n",
       "             2011  2012  2013  Total  \n",
       "Country                               \n",
       "Afghanistan  2203  2635  2004  58639  \n",
       "Albania       539   620   603  15699  \n",
       "Algeria      4325  3774  4331  69439  \n",
       "\n",
       "[3 rows x 38 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transformar a coluna 'Country' em índice no data frame\n",
    "df.set_index('Country', inplace=True)\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>imigracao</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ano  imigracao\n",
       "0  1980        211\n",
       "1  1981        220\n",
       "2  1982        192\n",
       "3  1983        139\n",
       "4  1984        145"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Criar lista com anos de 1980 a 2013 em forma de string\n",
    "anos = list(map(str,range(1980, 2014))) \n",
    "\n",
    "# Obter os dados de imigração do Brasil de 1980 a 2013\n",
    "brasil = df.loc['Brazil', anos]\n",
    "\n",
    "# Criar Dictionary com os dados de imigração do Brasil\n",
    "brasil_dict = {\n",
    "    'ano': brasil.index.tolist(),\n",
    "    'imigracao': brasil.values.tolist()\n",
    "}\n",
    "\n",
    "# Criar DataFrame com os dados de imigração do Brasil\n",
    "dados_brasil = pd.DataFrame(brasil_dict)\n",
    "dados_brasil.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exibir gráfico em que o eixo x é o ano e o eixo y é o número de imigrantes\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(dados_brasil['ano'], dados_brasil['imigracao'])\n",
    "\n",
    "plt.xticks(ticks=list(map(str, range(1980, 2014, 5))), rotation=90)\n",
    "plt.xlabel('Ano')\n",
    "\n",
    "plt.yticks(ticks=list(range(0, 3200, 200)))\n",
    "plt.ylabel('Número de Imigrantes')\n",
    "\n",
    "plt.title('Imigração Brasileira para o Canadá (1980-2013)')\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}