{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Graficos\n",
"\n",
"Graficos são uma forma muito interessante de visualizar informação, é um recurso amplamente utilizado em Data Science, Engenharia, Físcia, etc... Assume-se aqui netes capítulo que o aluno já possui o conhecimento prévio sobre gráficos e o funcionamento dos mesmos.\n",
"\n",
"Por padrão o python não vem equipado com suporte á criação de gráficos, mas por sorte, e pelo esforção de inumeros desenvolvedores, existem várias bibliotecas de apresentação de gráficos para o python.\n",
"\n",
"Neste capítulo iremos utilizar a biblioteca chamada matplotlib. A matplotlib tem suporte a uma gama quantidade de gráficos diferentes. Aqui nesta pagina você pode ver os gráficos que esta poderosa biblioteca possui. Como podemos ver pelo site da bibliteca, a mesma da suporte a muitos tipos de gráficos, e infelismente não temos como ver todos estes gráficos. Entretanto, uma coisa legal do site do matplotlib é que ele possui exemplos de código para cada gráfico, então é muito fácil entender o funcionamento de um gráfico novo.\n",
"\n",
"Se você esta utilizando um ambiente python puro será necessário isntalar o matplotlib, entrentanto se você utiliza o pacotão da anaconda ele já vem incluso =).\n",
"\n",
"Iremos cobrir dois tipos de gráficos aqui neste capítulo: Linhas e pontos. \n",
"\n",
"O primeiro passo para criarmos nossos gráficos é importar o matplotib para nosso script. Para isso é só escrever o seguinte:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Na linha anterior estamos dizendo para o python, importe a bilioteca do matplotlib com o nome plt, desta forma fica mais fácil de chamar os comandos de gráfico. Agora já temos suporte a gŕaficos nos nossos scripts, barbada.\n",
"\n",
"Bom, sempre que queremos trabalhar com gráficos precisaremos de dados, para isso, utilizaremos listas. Se você ainda não sabe utilizar listas confira o capítulo de Listas antes de seguir.\n",
"\n",
"Então vamos criar duas listas, uma contendo notas de alunos, e outra contendo o sexo de cada aluno. Observe que o sexo está representado como 0 e 1. \n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"notas = [10.0, 9.1, 3.2, 6.1, 8.9, 1.2, 0.5]\n",
"sexo = [1,0,0,1,1,0,0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"bacana, agora digamos que queremos visualizar estas notas e o sexo em um gráfico de pontos. Para isso utilizaremos o comando plot. \n",
"\n",
"O comando plot recebe os valores do eixo X e Y do gráfico.\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"notas = [10.0, 9.1, 3.2, 6.1, 8.9, 1.2, 0.5]\n",
"sexo = [1,0,0,1,1,0,0]\n",
"\n",
"plt.plot(notas, sexo)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Como voce pode ver o matplotlib não fez nada, o que esta certo, pois nao mandamos o mesmo mostrar o gráfico. Para mostar o gráfico utilizaremos o comando show."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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3l+H5OrL7M0K2LWGYbuXABU+RcsVioyMNGH4nC6jwjMHod2hkzl2IMziyeRW+qp2wK53v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\nAbq9hsbaC+GfAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"notas = [10.0, 9.1, 3.2, 6.1, 8.9, 1.2, 0.5]\n",
"sexo = [1,0,0,1,1,0,0]\n",
"\n",
"plt.plot(notas, sexo)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Bom... Temos um gráfico. Mas ele esta no formato de linhas, isso é pq o matplotlib assume por padrão que queremos um gráfico de linhas. Mas e se quisermos um gráfico de pontos?\n",
"\n",
"Para mudarmos o gráfico é só passar um \".\" a mais no plot, isso diz que queremos representar a informação como pontos.\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"notas = [10.0, 9.1, 3.2, 6.1, 8.9, 1.2, 0.5]\n",
"sexo = [1,0,0,1,1,0,0]\n",
"\n",
"plt.plot(notas, sexo,'.')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"bacana né? Agora, já podemos analizar nossa informação utilizando o gráfico. Claramente um dos sexos tira notas melhores que o outro, baseado nesta informação. Mas você concorda que fica ruim de ficar olhando o eixo y para saber qual o sexo do aluno? Não seria melhor mostar cada sexo com cores diferentes?\n",
"\n",
"Bom, podemos trocar a cor dos pontos utilizando a inicial da cor em ingles, por exemplo 'r', indica cor vermelha. Este valor deve ser passado junto com o ponto.\n",
"\n",
"Exemplo"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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lqUHGXZIaZNwlqUHGXZIa9D9KT9D7LW7OjwAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"notas = [10.0, 9.1, 3.2, 6.1, 8.9, 1.2, 0.5]\n",
"sexo = [1,0,0,1,1,0,0]\n",
"\n",
"plt.plot(notas, sexo,'r.')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Bom... trocamos a cor para vermelho, mas trocou para ambos os sexos... E agora? Bom, primeiro precisamos entender o que aconteceu. E em resumo, como as informações estão nas mesmas listas vao receber o mesmo tratamento no matplotlib. \n",
"\n",
"Mas então, como eu mudo as cores dos pontos só para um sexo? Simples, separamos em listas diferentes.\n",
"\n",
"Podemos fazer isso manualmente ou utilziando laços de repetição, vamos utilizar laços aqui pq né, é mais interessante e prático.\n",
"\n",
"Exemplo:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"notas = [10.0, 9.1, 3.2, 6.1, 8.9, 1.2, 0.5]\n",
"sexo = [1,0,0,1,1,0,0]\n",
"\n",
"nSexo0 = []\n",
"nSexo1 = []\n",
"\n",
"sSexo0 = []\n",
"sSexo1 = []\n",
"\n",
"for i in range(0, len(sexo)):\n",
" if sexo[i] == 0:\n",
" nSexo0.append(notas[i])\n",
" sSexo0.append(0)\n",
" else:\n",
" nSexo1.append(notas[i])\n",
" sSexo1.append(1)\n",
"\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pronto, agora temos as notas separadas por sexo, observe que mantivemos uma lista que diz qual é o valor do sexo de cada pessoa, isso é para manter o eixo y com esta escala.\n",
"\n",
"Agora é só mostrar estes dados, o matplotlib permite que sejam dados inúmeros plots antes do gráfico ser mostrado, portanto podemos dar um plot para o sexo0 com uma cor e outro plot com sexo1 com outra.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"notas = [10.0, 9.1, 3.2, 6.1, 8.9, 1.2, 0.5]\n",
"sexo = [1,0,0,1,1,0,0]\n",
"\n",
"nSexo0 = []\n",
"nSexo1 = []\n",
"\n",
"sSexo0 = []\n",
"sSexo1 = []\n",
"\n",
"for i in range(0, len(sexo)):\n",
" if sexo[i] == 0:\n",
" nSexo0.append(notas[i])\n",
" sSexo0.append(0)\n",
" else:\n",
" nSexo1.append(notas[i])\n",
" sSexo1.append(1)\n",
"plt.plot(nSexo0, sSexo0, '.g')\n",
"plt.plot(nSexo1, sSexo1, '.r')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"E agora podemos ver que cada sexo tem uma cor diferente no gráfico. Neste caso podemos ver que o sexo da cor vermelha vai normalmente melhor que o da cor verde. \n",
"\n",
"Mas este gráfico ainda esta muito simples, podemos melhora-lo colocando um título e indentificação de cada eixo.\n",
"\n",
"Para adicionar um título utilizamos o comando title. E para os eixos os comandos xlabel e ylabel.\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"notas = [10.0, 9.1, 3.2, 6.1, 8.9, 1.2, 0.5]\n",
"sexo = [1,0,0,1,1,0,0]\n",
"\n",
"nSexo0 = []\n",
"nSexo1 = []\n",
"\n",
"sSexo0 = []\n",
"sSexo1 = []\n",
"\n",
"for i in range(0, len(sexo)):\n",
" if sexo[i] == 0:\n",
" nSexo0.append(notas[i])\n",
" sSexo0.append(0)\n",
" else:\n",
" nSexo1.append(notas[i])\n",
" sSexo1.append(1)\n",
" \n",
"plt.plot(nSexo0, sSexo0, '.g')\n",
"plt.plot(nSexo1, sSexo1, '.r')\n",
"plt.xlabel('nota')\n",
"plt.ylabel('sexo')\n",
"plt.title('Distribuicao de notas por sexo')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Bacana né, agora vamos colocar uma legenda. Para isso é só adicionarmos o comando label em cada plo, o valor de legenda destes dados e a localização da legenda.\n",
"\n",
"Para indicar a localização da legenda utilizamos o comando plt.legend(loc='local da legenda')\n",
"\n",
"Existem inúmeros locais possiveis:\n",
"'upper right' : 1,\n",
"'upper left' : 2,\n",
"'lower left' : 3,\n",
"'lower right' : 4,\n",
"'right' : 5,\n",
"'center left' : 6,\n",
"'center right' : 7,\n",
"'lower center' : 8,\n",
"'upper center' : 9,\n",
"'center' : 10,\n",
"\n",
"Exemplo:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"notas = [10.0, 9.1, 3.2, 6.1, 8.9, 1.2, 0.5]\n",
"sexo = [1,0,0,1,1,0,0]\n",
"\n",
"nSexo0 = []\n",
"nSexo1 = []\n",
"\n",
"sSexo0 = []\n",
"sSexo1 = []\n",
"\n",
"for i in range(0, len(sexo)):\n",
" if sexo[i] == 0:\n",
" nSexo0.append(notas[i])\n",
" sSexo0.append(0)\n",
" else:\n",
" nSexo1.append(notas[i])\n",
" sSexo1.append(1)\n",
" \n",
"plt.plot(nSexo0, sSexo0, '.g', label='sexo 0')\n",
"plt.plot(nSexo1, sSexo1, '.r', label='sexo 1')\n",
"plt.xlabel('nota')\n",
"plt.ylabel('sexo')\n",
"plt.title('Distribuicao de notas por sexo')\n",
"plt.legend(loc='center right')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Bom, já sabemos o básico para gráficos de pontos, agora vamos criar um gráfico de linhas. Digamos que, por exemplo queremos mostar o grafico de uma função, digamos que, a função de coseno. O primeiro passo é termos a informação. \n",
"\n",
"Para facilitar o calculo do coseno vamos utilizar a biblioteca de matematica, esta biblioteca da suporte a inúmeras funções diferentes. Aqui você pode ver todas as funções desta biblioteca: https://docs.python.org/2/library/math.html\n",
"\n",
"Bom, vamos começar criando uma lista com os valores de cosseno. Para isso primeiro importamos o coseno da biblioteca de matematica. E depois criamos uma lista utilizando ele e um laço de repetição.\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from math import cos #importamos o cosseno\n",
"\n",
"fCos = [] #lista que vai guardar os valores de cosseno\n",
"\n",
"#vamos gerar 1000 valores de coseno\n",
"for i in range(0,1000):\n",
" valor = cos(i*0.1)\n",
" fCos.append(valor)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Agora que temos uma lista com os valores de cosseno, podemos mostrar a mesma em um grafico de linhas. Portanto vamos importar o matplotlib e plotar esta lista."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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H45mMrUd0GNqNGlr1mnouZ1BSe+4waPMyMeXiIOlwGoaABNDMA0gE+siKfqKXvYmwZF66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\nnjTGDAF8AMC9yWf3Anhf8vf7APxIyHhi0DedXp/9fcfxsEV7V4rXy0+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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from math import cos #importamos o cosseno\n",
"import matplotlib.pyplot as plt\n",
"\n",
"fCos = [] #lista que vai guardar os valores de cosseno\n",
"\n",
"#vamos gerar 1000 valores de coseno\n",
"for i in range(0,1000):\n",
" valor = cos(i*0.1)\n",
" fCos.append(valor)\n",
" \n",
" \n",
"plt.plot(fCos)\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"E temos o gráfico da função cos. Podemos diminuir a frequencia dela aumentando o valor multiplicado em i."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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JLTVNbVaHoyzyTmE1DRc7+LSPVCNdcvuMZERg/cGzVoeiBsgVJYa5QIljms524AVg1Qgc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\nRK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from math import cos #importamos o cosseno\n",
"import matplotlib.pyplot as plt\n",
"\n",
"fCos = [] #lista que vai guardar os valores de cosseno\n",
"\n",
"#vamos gerar 1000 valores de coseno\n",
"for i in range(0,1000):\n",
" valor = cos(i*0.01)\n",
" fCos.append(valor)\n",
" \n",
" \n",
"plt.plot(fCos)\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ou usar o i para aumentar loucamente o valor de y..."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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+T0JLJTBe1IQ9CSZ0jxQ2nv5ZF1eRIjhWu9DrSbi1DQLzpKu6iWw6gXSy979p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\nubkPxgDoAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from math import cos #importamos o cosseno\n",
"import matplotlib.pyplot as plt\n",
"\n",
"fCos = [] #lista que vai guardar os valores de cosseno\n",
"\n",
"#vamos gerar 1000 valores de coseno\n",
"for i in range(0,1000):\n",
" valor = cos(i*0.1)*i\n",
" fCos.append(valor)\n",
" \n",
" \n",
"plt.plot(fCos)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Podemos mostrar o seno tbm."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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X05sjLk4qHCxdqlqJAhITpcvUqJFqJQGRmCh17UJwa/lG0aJAhw7y29cwAKNW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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from math import cos #importamos o cosseno\n",
"from math import sin #importamos o seno\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"fCos = [] #lista que vai guardar os valores de cosseno\n",
"fSin = [] #lista que vai guardar os valores de seno\n",
"\n",
"#vamos gerar 1000 valores de coseno\n",
"for i in range(0,1000):\n",
" valorCos = cos(i*0.01)\n",
" valorSin = sin(i*0.01)\n",
" \n",
" fCos.append(valorCos)\n",
" fSin.append(valorSin)\n",
" \n",
" \n",
"plt.plot(fCos, 'r')\n",
"plt.plot(fSin, 'b') #muda a cor para blue\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Como podemos ver, trabalhar com graficos em python é mto simples. Obiviamente vimos apenas o básico do matplotlib, mas existe muito mais que pode ser feito com esta poderosa biblioteca."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}