{
"cells": [
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"cell_type": "code",
"execution_count": null,
"id": "7553056d",
"metadata": {},
"outputs": [],
"source": [
"import plotly.express as px\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"id": "7c19f156",
"metadata": {},
"source": [
"### 1. Criando um DataFrame de exemplo"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "14b28808",
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame({\n",
" \"Dias\": [\"Seg\", \"Ter\", \"Qua\", \"Qui\", \"Sex\", \"Sab\", \"Dom\"],\n",
" \"Vendas\": [250, 300, 350, 200, 450, 600, 500],\n",
" \"Clima\": [\"Chuvoso\", \"Nublado\", \"Ensolarado\", \"Chuvoso\", \"Ensolarado\", \"Ensolarado\", \"Nublado\"]\n",
"})"
]
},
{
"cell_type": "markdown",
"id": "3b85bcad",
"metadata": {},
"source": [
"### 2. Criando o gráfico de dispersão com linha (Scatter)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "4f434ec0",
"metadata": {},
"outputs": [],
"source": [
"fig = px.line(\n",
" df, \n",
" x=\"Dias\", \n",
" y=\"Vendas\", \n",
" color=\"Clima\", \n",
" title=\"Vendas Semanais por Clima\",\n",
" markers=True\n",
")"
]
},
{
"cell_type": "markdown",
"id": "644b08c5",
"metadata": {},
"source": [
"### 3. Exibindo o gráfico"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "0e7c8292",
"metadata": {},
"outputs": [
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