DashPlotly.ipynb

DashPlotly.ipynb

{
  "cells": [
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import dash\nimport dash_html_components as html\nimport dash_table\nfrom jupyter_dash import JupyterDash\nimport pandas as pd\nfrom collections import OrderedDict\n\napp = JupyterDash(__name__)\n\ndf = pd.DataFrame(OrderedDict([\n    ('Lock-offs', ['One-Arm Negatives', 'Two-Arm Rowers', 'One-Arm Locks on Rings',\n                   'Frenchies','One-Arm Pull-Ups', 'Shoulder Pumps']),\n    ('Reps', ['3', '4', '5', '6',\"7\",\"8\"]),\n    ('Sets', ['3', '4', '5', '6',\"7\",\"8\"])\n]))\n\n\napp.layout = html.Div([\n    dash_table.DataTable(\n        id='table-dropdown',\n        data=df.to_dict('records'),\n        columns=[\n            {'id': 'Lock-offs', 'name': 'Lock-offs', 'presentation': 'dropdown'},\n            {'id': 'Reps',      'name': 'Reps',      'presentation': 'dropdown'},\n            {'id': 'Sets',      'name': 'Sets',      'presentation': 'dropdown'},\n        ],\n\n        editable=True,\n        dropdown={\n            'Lock-offs': {\n                'options': [\n                    {'label': i, 'value': i}\n                    for i in df['Lock-offs'].unique()\n                ]\n            },\n            'Reps': {\n                'options': [\n                    {'label': i, 'value': i}\n                    for i in df['Reps'].unique()\n                ]\n            },\n            'Sets': {\n                 'options': [\n                    {'label': i, 'value': i}\n                    for i in df['Sets'].unique()\n                ]\n            }\n        }\n    ),\n    html.Div(id='table-dropdown-container')\n])\n\n\napp.run_server(mode='inline')",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.lib.display.IFrame at 0x7f851e09d3d0>",
            "text/html": "\n        <iframe\n            width=\"100%\"\n            height=\"650\"\n            src=\"http://127.0.0.1:8050/\"\n            frameborder=\"0\"\n            allowfullscreen\n        ></iframe>\n        "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.3",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "4d43a2bd3b0bdbaaee0996656c840369",
      "data": {
        "description": "DashPlotly.ipynb",
        "public": true
      }
    },
    "_draft": {
      "nbviewer_url": "https://gist.github.com/4d43a2bd3b0bdbaaee0996656c840369"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}