{
  "metadata": {
    "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"
    },
    "kernelspec": {
      "name": "python",
      "display_name": "Pyolite",
      "language": "python"
    }
  },
  "nbformat_minor": 4,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "source": "# Plotly in JupyterLite\n\n`plotly.py` is an interactive, open-source, and browser-based graphing library for Python: https://plotly.com/python/",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Install the dependencies",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import micropip\nawait micropip.install('plotly')\n\n# set the default renderer\nimport os\nos.environ[\"PLOTLY_RENDERER\"] = \"jupyterlab\"",
      "metadata": {
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "## Basic Figure",
      "metadata": {
        "tags": []
      }
    },
    {
      "cell_type": "code",
      "source": "import plotly.graph_objects as go\nfig = go.Figure()\nfig.add_trace(go.Scatter(y=[2, 1, 4, 3]))\nfig.add_trace(go.Bar(y=[1, 4, 3, 2]))\nfig.update_layout(title = 'Hello Figure')\nfig.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "## Basic Table with a Pandas DataFrame",
      "metadata": {
        "tags": []
      }
    },
    {
      "cell_type": "code",
      "source": "import plotly.graph_objects as go\nimport pandas as pd\n\nfrom js import fetch\n\nURL = \"https://raw.githubusercontent.com/plotly/datasets/master/2014_usa_states.csv\"\n\nres = await fetch(URL)\ntext = await res.text()\n\nfilename = 'data.csv'\n\nwith open(filename, 'w') as f:\n    f.write(text)\n\ndf = pd.read_csv(filename)\n\nfig = go.Figure(data=[go.Table(\n    header=dict(values=list(df.columns),\n                fill_color='paleturquoise',\n                align='left'),\n    cells=dict(values=[df.Rank, df.State, df.Postal, df.Population],\n               fill_color='lavender',\n               align='left'))\n])\n\nfig.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "## Quiver Plot with Points",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import plotly.figure_factory as ff\nimport plotly.graph_objects as go\n\nimport numpy as np\n\nx,y = np.meshgrid(np.arange(-2, 2, .2),\n                  np.arange(-2, 2, .25))\nz = x*np.exp(-x**2 - y**2)\nv, u = np.gradient(z, .2, .2)\n\n# Create quiver figure\nfig = ff.create_quiver(x, y, u, v,\n                       scale=.25,\n                       arrow_scale=.4,\n                       name='quiver',\n                       line_width=1)\n\n# Add points to figure\nfig.add_trace(go.Scatter(x=[-.7, .75], y=[0,0],\n                    mode='markers',\n                    marker_size=12,\n                    name='points'))\n\nfig.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}