demo/Visualize with Power BI demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ce1426e9",
   "metadata": {},
   "source": [
    "# Demo - Create Power BI Reports Quickly in Jupyter Notebooks\n",
    "The following notebook demonstrates how to quickly create Power BI reports from your data in Jupyter notebooks. \n",
    "The notebook will walk through the steps of authenticating to Power BI, importing and processing data, and auto-generating a Power BI report, all within the notebook.\n",
    "\n",
    "These reports can be used to explore your data, or as a starting point for creating a custom report. Interacting with the report is simple and empowers you to explore your data with just a few simple steps.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e44c1a00-3f29-4db8-8c18-d6a145875e4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from powerbiclient import QuickVisualize, get_dataset_config, Report\n",
    "from powerbiclient.authentication import DeviceCodeLoginAuthentication\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "784a34f9",
   "metadata": {},
   "source": [
    "### Authenticate to Power BI\n",
    "\n",
    "#### The following authentication methods can be used to authenticate the user and get the access token:\n",
    "\n",
    "- [Device flow authentication](https://msal-python.readthedocs.io/en/latest/#msal.PublicClientApplication.acquire_token_by_device_flow)\n",
    "    \n",
    "    Code snippet:\n",
    "    ``` py\n",
    "  from powerbiclient.authentication import DeviceCodeLoginAuthentication\n",
    "  device_auth = DeviceCodeLoginAuthentication()\n",
    "    ```\n",
    "- [Interactive authentication](https://msal-python.readthedocs.io/en/latest/#msal.PublicClientApplication.acquire_token_interactive)\n",
    "    \n",
    "    Code snippet:\n",
    "    ``` py\n",
    "  from powerbiclient.authentication import InteractiveLoginAuthentication\n",
    "  interactive_auth = InteractiveLoginAuthentication()    \n",
    "    ```\n",
    "\n",
    "Note: Either use auth object created or access token acquired above while creating the report instance\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81ce840c",
   "metadata": {},
   "source": [
    "#### Run the cell below and follow the steps in the output to obtain the auth object using the Device Flow Authentication method for the demo notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9b1cd26",
   "metadata": {},
   "outputs": [],
   "source": [
    "device_auth = DeviceCodeLoginAuthentication()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93c8d2fd",
   "metadata": {},
   "source": [
    "### Import and process data\n",
    "The code below is used to import a sample dataset as a pandas DataFrame and preform basic preprocessing on it. Make sure you've downloaded the sample csv file, located in the Demo folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4eb1d652",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import sample CSV as a pandas DataFrame\n",
    "df = pd.read_csv('Financial Sample.csv')\n",
    "\n",
    "# Display DataFrame\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69edf996",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocessing - remove columns, limit to products with over 1000 units sold, and create a binary column indicating whether a product was discounted or not\n",
    "df = df.drop(['Month Number', 'Month Name', 'Year'], axis=1)\n",
    "df = df.loc[df['Units Sold'] > 1000]\n",
    "df = df.fillna('')\n",
    "df['Discounted'] = df['Discount Band'] != ''\n",
    "\n",
    "#Display updated DataFrame\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7d74b55",
   "metadata": {},
   "source": [
    "### Create quick Power BI visualization\n",
    "Run the code below to use the authentication object and DataFrame obtained above to create a Power BI report and render the report to the notebook.\n",
    "The output will be an autogenerated report within a few seconds. You can interact with the report, configure the data fields, edit the report layout, and save the report to Power BI.\n",
    "\n",
    "Learn more here - [Interacting with autogenerated \"quick\" reports](https://learn.microsoft.com/en-us/power-bi/create-reports/service-interact-quick-report)\n",
    "\n",
    "To create a new report, simply run the cells below again."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07198839",
   "metadata": {},
   "source": [
    "Create a report instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40bf4ad9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Power BI report from your data\n",
    "PBI_visualize = QuickVisualize(get_dataset_config(df), auth=device_auth)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c440583",
   "metadata": {},
   "source": [
    "### Power BI event handlers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a0b58be",
   "metadata": {},
   "source": [
    "#### Set quick visualization loaded event handler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8690985f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def loaded_callback(event_details):\n",
    "    print('Quick visualize has loaded')\n",
    "\n",
    "PBI_visualize.on('loaded', loaded_callback)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5f1fc92",
   "metadata": {},
   "source": [
    "#### Set quick visualization rendered event handler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7493d09e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rendered_callback(event_details):\n",
    "    print('Quick visualize has rendered')\n",
    "\n",
    "PBI_visualize.on('rendered', rendered_callback)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93e2ffef",
   "metadata": {},
   "source": [
    "#### Set quick visualization saved event handler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a8e6f86",
   "metadata": {},
   "outputs": [],
   "source": [
    "def saved_callback(event_details):\n",
    "    print('Quick visualize has saved the report')\n",
    "\n",
    "PBI_visualize.on('saved', saved_callback)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb3d0946",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Render new report\n",
    "PBI_visualize"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4784dc27",
   "metadata": {},
   "source": [
    "### Configure report size\n",
    "Run the following code to configure the size of the embedded report output, to ensure the best fit in your notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c6b6c85",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set new width and height for the container\n",
    "PBI_visualize.set_size(400, 600)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fb09069",
   "metadata": {},
   "source": [
    "### Load saved report\n",
    "Once you're happy with your report, you can either keep it in the notebook, or save it to Power BI. The code block below demonstrates how to load the report again using the Power BI library after you've saved it. This can be done as long as the kernel is still active. \n",
    "Alternatively, you can load any report using the group_id and report_id parameters, as explained [here](https://github.com/microsoft/powerbi-jupyter/blob/main/DOCUMENTATION.md#__init__-report)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a1b8596",
   "metadata": {},
   "outputs": [],
   "source": [
    "report = PBI_visualize.get_saved_report()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0642fc7",
   "metadata": {},
   "source": [
    "### Load report in output cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9019759c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#render report \n",
    "report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "014b1574",
   "metadata": {},
   "source": [
    "You can interact with this report as part of your notebook. To learn more, visit our [documentation](https://github.com/microsoft/powerbi-jupyter/blob/main/DOCUMENTATION.md#PowerBI-report-embedding-widget) or the report embedding [demo](https://github.com/microsoft/powerbi-jupyter/blob/main/demo/Embed%20Power%20BI%20report%20demo.ipynb)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.11"
  },
  "toc-autonumbering": true,
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}