Mamografia_ parda_SP.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#importar o mat \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#colocar o conjunto de dados que vai plotar os dados \n",
    "labels = [2019, 2020, 2021, 2022, 2023]\n",
    "vals = [37.070, 22.556, 29.224, 32.433, 38.216]\n",
    "\n",
    "#construção dos gráficos\n",
    "fig, ax = plt.subplots(figsize= (8, 8))\n",
    "\n",
    "# tipo do gráfico\n",
    "ax.bar(labels, vals)\n",
    "\n",
    "#nome do titulo 1 em cima no meio\n",
    "ax.set_title( 'Número de mamografias realizadas pela população parda em SP', fontsize=13)\n",
    "#nome do titulo 2 na vertical lado esquerdo\n",
    "ax.set_ylabel( \"Pacientes (valores de 0 a 38.216 mil)\" , fontsize=13 )\n",
    "# titulo embaixo no meio\n",
    "ax.set_xlabel( 'Ano' , fontsize=13 )\n",
    "\n",
    "\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": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}