{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Ficha técnica\n", "\n", "__identificador__: aulaP6;\n", "\n", "__título__: Representação gráfica de informação;\n", "\n", "__data início__: 2021-11-15;\n", "\n", "__autor__: José Carlos Ramalho, D1513;\n", "\n", "__resumo__: Nesta aula, irás aprender a representar conjuntos de dados de forma gráfica. A ficha tem pequenos exemplos\n", "e propõe alguns exercícios de consolidação." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot\n", "Para a representação gráfica iremos usar o módulo plot.\n", "\n", "### Instalação\n", "`pip install matplotlib`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot1: uma espécie de \"Hello world!\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# importing the required module\n", "import matplotlib.pyplot as plt\n", " \n", "# x axis values\n", "x = [1,2,3]\n", "# corresponding y axis values\n", "y = [2,4,1]\n", " \n", "# plotting the points\n", "plt.plot(x, y)\n", " \n", "# naming the x axis\n", "plt.xlabel('Abcissas')\n", "# naming the y axis\n", "plt.ylabel('Ordenadas')\n", " \n", "# giving a title to my graph\n", "plt.title('O meu primeiro gráfico!')\n", " \n", "# function to show the plot\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot2: duas funções no mesmo gráfico" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", " \n", "# line 1 points\n", "x1 = [1,2,3]\n", "y1 = [2,4,1]\n", "# plotting the line 1 points\n", "plt.plot(x1, y1, label = \"linha 1\")\n", " \n", "# line 2 points\n", "x2 = [1,2,3]\n", "y2 = [4,1,3]\n", "# plotting the line 2 points\n", "plt.plot(x2, y2, label = \"linha 2\")\n", " \n", "# naming the x axis\n", "plt.xlabel('Abcissas')\n", "# naming the y axis\n", "plt.ylabel('Ordenadas')\n", "# giving a title to my graph\n", "plt.title('Gráfico com duas funções')\n", " \n", "# show a legend on the plot\n", "plt.legend()\n", " \n", "# function to show the plot\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot3: configurações" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", " \n", "# x axis values\n", "x = [1,2,3,4,5,6]\n", "# corresponding y axis values\n", "y = [2,4,1,5,2,6]\n", " \n", "# plotting the points\n", "plt.plot(x, y, color='green', linestyle='dashed', linewidth = 3,\n", " marker='o', markerfacecolor='blue', markersize=12)\n", " \n", "# setting x and y axis range\n", "plt.ylim(1,8)\n", "plt.xlim(1,8)\n", " \n", "# naming the x axis\n", "plt.xlabel('Abcissas')\n", "# naming the y axis\n", "plt.ylabel('Ordenadas')\n", " \n", "# giving a title to my graph\n", "plt.title('Um gráfico com estilo!')\n", " \n", "# function to show the plot\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Primeiros exercícios\n", "### Exercício 1: desenha um quadrado centrado na origem com 5 unidades de lado" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Escreve aqui o teu código" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercício 2: desenha n quadrados centrados na origem com lados 1..10" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Escreve aqui o teu código" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercício 3: representa graficamente os seguintes polinómios\n", "* 2x - 5\n", "* x^2 - 7\n", "* x^3 - 2x^2 + 1\n", "\n", "Para isso:\n", "* Descarrega o módulo polinómio, desenvolvido pelo docente;\n", "* Cria um programa e define os 3 polinómios;\n", "* Cria uma representação gráfica para: x = x; y = plo(x);\n", "* Em que x = [-100, 100] e y = [-200, 200];\n", "* Visualiza os resultados e adapta a escala dos eixos para uma melhor visualização de cada polinómio." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gráficos de barras\n", "### Barras 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", " \n", "# x-coordinates of left sides of bars\n", "left = [1, 2, 3, 4, 5]\n", " \n", "# heights of bars\n", "height = [6, 4, 8, 12, 5]\n", " \n", "# labels for bars\n", "tick_label = ['cebolas', 'maçãs', 'pêras', 'batatas', 'cenouras']\n", " \n", "# plotting a bar chart\n", "plt.bar(left, height, tick_label = tick_label,\n", " width = 0.8, color = ['red', 'green'])\n", " \n", "# naming the x-axis\n", "plt.xlabel('Frutas e legumes')\n", "# naming the y-axis\n", "plt.ylabel('Quantidade')\n", "# plot title\n", "plt.title('O meu stock de verdes')\n", " \n", "# function to show the plot\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercício 4: estatísticas de alunos\n", "Vamos usar o módulo Pandas para ler o dataset de alunos disponível no site da UC.\n", "\n", "__Instalação:__ `pip install pandas`\n", "\n", "__A fazer:__ Depois de analisares os exemplos seguintes, constrói um programa que desenha um gráfico de barras com os alunos de cada curso." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " id_aluno nome curso tpc1 tpc2 tpc3 tpc4\n", "0 a1 Aysha Melanie Gilberto LEI 12 8 19 8\n", "1 a2 Igor André Cantanhede ENGFIS 12 16 18 20\n", "2 a3 Laurénio Narciso ENGFIS 8 14 15 14\n", "3 a4 Jasnoor Casegas LCC 14 20 17 11\n", "4 a5 Tawseef Rebouças ENGBIOM 13 14 13 17\n", ".. ... ... ... ... ... ... ...\n", "95 a96 Anaïs Sintra LCC 19 19 12 9\n", "96 a97 Salvador Banaca LCC 12 9 20 12\n", "97 a98 Guilherme Matias Almeirão ENGFIS 14 9 12 11\n", "98 a99 Xavier Luís Bulha ENGFIS 17 13 8 10\n", "99 a100 Tude Searas LEI 20 17 8 14\n", "\n", "[100 rows x 7 columns]\n" ] } ], "source": [ "import pandas as pd\n", "alunos = pd.read_csv(\"../programas/plot/alunos.csv\")\n", "print(alunos)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "alunos[\"tpc1\"].mean()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "alunos[[\"tpc1\", \"tpc2\", \"tpc3\",\"tpc4\"]].mean()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "alunos[[\"tpc1\", \"tpc2\", \"tpc3\",\"tpc4\"]].describe()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "alunos[[\"curso\", \"tpc1\"]].groupby(\"curso\").mean()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cont = alunos[\"curso\"].value_counts()\n", "print(cont.get('ENGFIS'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cursos = alunos[\"curso\"].unique()\n", "print(cursos)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "cont = alunos[\"curso\"].value_counts().plot(kind='bar')" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cont2 = alunos[\"curso\"].value_counts().plot(kind='pie')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gráficos em forma de tarte" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", " \n", "# defining labels\n", "activities = ['eat', 'sleep', 'work', 'play']\n", " \n", "# portion covered by each label\n", "slices = [3, 7, 8, 6]\n", " \n", "# color for each label\n", "colors = ['r', 'y', 'g', 'b']\n", " \n", "# plotting the pie chart\n", "plt.pie(slices, labels = activities, colors=colors,\n", " startangle=90, shadow = True, explode = (0, 0, 0.1, 0),\n", " radius = 1.2, autopct = '%1.1f%%')\n", " \n", "# plotting legend\n", "plt.legend()\n", " \n", "# showing the plot\n", "plt.show()" ] } ], "metadata": { "interpreter": { "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49" }, "kernelspec": { "display_name": "Python 3.9.1 64-bit", "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": "3.9.1" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }