{ "cells": [ { "cell_type": "markdown", "id": "683fc853", "metadata": {}, "source": [ "# Gráficos interactivos con Plotly\n", "\n", "Objetivos: burbujas con varias variables, subgráficos, animaciones y gráficos 3D, exportados a HTML. Usa `gapminder`, incluido en Plotly. Refuerza el quiz de Plotly." ] }, { "cell_type": "code", "execution_count": 1, "id": "26f5f55c", "metadata": { "execution": { "iopub.execute_input": "2026-09-29T14:55:05.829894Z", "iopub.status.busy": "2026-09-29T14:55:05.829598Z", "iopub.status.idle": "2026-09-29T14:55:06.565079Z", "shell.execute_reply": "2026-09-29T14:55:06.564712Z" } }, "outputs": [ { "data": { "text/html": [ "
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countrycontinentyearlifeExppopgdpPercapiso_alphaiso_num
0AfghanistanAsia195228.8018425333779.445314AFG4
1AfghanistanAsia195730.3329240934820.853030AFG4
2AfghanistanAsia196231.99710267083853.100710AFG4
3AfghanistanAsia196734.02011537966836.197138AFG4
4AfghanistanAsia197236.08813079460739.981106AFG4
\n", "
" ], "text/plain": [ " country continent year lifeExp pop gdpPercap iso_alpha \\\n", "0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG \n", "1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG \n", "2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG \n", "3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG \n", "4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG \n", "\n", " iso_num \n", "0 4 \n", "1 4 \n", "2 4 \n", "3 4 \n", "4 4 " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import plotly.express as px\n", "import plotly.graph_objects as go\n", "import plotly.io as pio\n", "from plotly.subplots import make_subplots\n", "\n", "pio.renderers.default = \"notebook_connected\"\n", "df = px.data.gapminder()\n", "df.head()" ] }, { "cell_type": "markdown", "id": "d51dff6f", "metadata": {}, "source": [ "## Burbujas con varias variables\n", "\n", "Posición, tamaño, color y texto al pasar el ratón. `log_x=True` para valores que abarcan varios órdenes de magnitud." ] }, { "cell_type": "code", "execution_count": 2, "id": "905f8274", "metadata": { "execution": { "iopub.execute_input": "2026-09-29T14:55:06.571189Z", "iopub.status.busy": "2026-09-29T14:55:06.571066Z", "iopub.status.idle": "2026-09-29T14:55:06.793560Z", "shell.execute_reply": "2026-09-29T14:55:06.793036Z" } }, "outputs": [ { "data": { "text/html": [ " \n", " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d2007 = df[df[\"year\"] == 2007]\n", "fig = px.scatter(d2007, x=\"gdpPercap\", y=\"lifeExp\", size=\"pop\", color=\"continent\",\n", " hover_name=\"country\", log_x=True, size_max=45,\n", " labels={\"gdpPercap\": \"PIB per cápita\", \"lifeExp\": \"Esperanza de vida\"},\n", " title=\"Riqueza y esperanza de vida (2007)\")\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "5155c681", "metadata": {}, "source": [ "## Subgráficos" ] }, { "cell_type": "code", "execution_count": 3, "id": "2950b335", "metadata": { "execution": { "iopub.execute_input": "2026-09-29T14:55:06.799294Z", "iopub.status.busy": "2026-09-29T14:55:06.799062Z", "iopub.status.idle": "2026-09-29T14:55:06.841838Z", "shell.execute_reply": "2026-09-29T14:55:06.841382Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mexico = df[df[\"country\"] == \"Mexico\"]\n", "fig = make_subplots(rows=1, cols=2, subplot_titles=(\"Esperanza de vida\", \"Población\"))\n", "fig.add_trace(go.Scatter(x=mexico[\"year\"], y=mexico[\"lifeExp\"], mode=\"lines+markers\"), row=1, col=1)\n", "fig.add_trace(go.Bar(x=mexico[\"year\"], y=mexico[\"pop\"]), row=1, col=2)\n", "fig.update_layout(title=\"México en el tiempo\", showlegend=False)\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "0cec55b8", "metadata": {}, "source": [ "## Animación\n", "\n", "Fija `range_x` y `range_y` para que la escala no cambie entre fotogramas." ] }, { "cell_type": "code", "execution_count": 4, "id": "e7cbafaa", "metadata": { "execution": { "iopub.execute_input": "2026-09-29T14:55:06.846952Z", "iopub.status.busy": "2026-09-29T14:55:06.846831Z", "iopub.status.idle": "2026-09-29T14:55:07.101368Z", "shell.execute_reply": "2026-09-29T14:55:07.100856Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.scatter(df, x=\"gdpPercap\", y=\"lifeExp\", size=\"pop\", color=\"continent\",\n", " hover_name=\"country\", animation_frame=\"year\", log_x=True, size_max=45,\n", " range_x=[100, 100000], range_y=[25, 90])\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "e6f39b10", "metadata": {}, "source": [ "## Gráficos 3D\n", "\n", "En 3D los títulos de los ejes van dentro de `scene`." ] }, { "cell_type": "code", "execution_count": 5, "id": "2d79dc46", "metadata": { "execution": { "iopub.execute_input": "2026-09-29T14:55:07.109743Z", "iopub.status.busy": "2026-09-29T14:55:07.109646Z", "iopub.status.idle": "2026-09-29T14:55:07.193872Z", "shell.execute_reply": "2026-09-29T14:55:07.193277Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rng = np.random.default_rng(42)\n", "n = 200\n", "precio = rng.uniform(10, 100, n)\n", "publicidad = rng.uniform(0, 50, n)\n", "ventas = 500 - 3 * precio + 6 * publicidad + rng.normal(0, 20, n)\n", "fig = px.scatter_3d(x=precio, y=publicidad, z=ventas, color=ventas,\n", " labels={\"x\": \"precio\", \"y\": \"publicidad\", \"z\": \"ventas\"},\n", " title=\"Ventas según precio y publicidad\")\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 6, "id": "3a6e70bb", "metadata": { "execution": { "iopub.execute_input": "2026-09-29T14:55:07.199222Z", "iopub.status.busy": "2026-09-29T14:55:07.199115Z", "iopub.status.idle": "2026-09-29T14:55:08.360249Z", "shell.execute_reply": "2026-09-29T14:55:08.355997Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace(-3, 3, 60)\n", "X, Y = np.meshgrid(x, x)\n", "fig = go.Figure(go.Surface(x=x, y=x, z=np.sin(X) * np.cos(Y), colorscale=\"Viridis\"))\n", "fig.update_layout(title=\"Superficie z = sen(x)·cos(y)\",\n", " scene={\"xaxis_title\": \"x\", \"yaxis_title\": \"y\", \"zaxis_title\": \"z\"})\n", "fig.write_html(\"superficie.html\")\n", "fig.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": "3.13.14" } }, "nbformat": 4, "nbformat_minor": 5 }