{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "141c3586-4c3b-4d10-96bc-f7af7195a0dd",
   "metadata": {},
   "source": [
    "## bia-bob demo\n",
    "stackview is compatible with [bia-bob](https://github.com/haesleinhuepf/bia-bob) providing hints to bob how to use it. This might be relevant, e.g. if you aim to generate custom interactive widgets to explore your data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "99be5861-af31-4668-8323-7aab4765a5ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import bia_bob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ed73f92a-09fb-4d71-bef7-63acf0437584",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>island</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.5</td>\n",
       "      <td>17.4</td>\n",
       "      <td>186.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>40.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>36.7</td>\n",
       "      <td>19.3</td>\n",
       "      <td>193.0</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
       "0  Adelie  Torgersen            39.1           18.7              181.0   \n",
       "1  Adelie  Torgersen            39.5           17.4              186.0   \n",
       "2  Adelie  Torgersen            40.3           18.0              195.0   \n",
       "3  Adelie  Torgersen             NaN            NaN                NaN   \n",
       "4  Adelie  Torgersen            36.7           19.3              193.0   \n",
       "\n",
       "   body_mass_g     sex  \n",
       "0       3750.0    Male  \n",
       "1       3800.0  Female  \n",
       "2       3250.0  Female  \n",
       "3          NaN     NaN  \n",
       "4       3450.0  Female  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "# Load the penguin dataset\n",
    "penguins_df = sns.load_dataset('penguins')\n",
    "\n",
    "# Display the first few rows of the DataFrame\n",
    "penguins_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4f4bad3-2184-4017-b061-a870b878b55d",
   "metadata": {},
   "source": [
    "You can ask `%bob` to visualize data for you. In case `stackview` is installed and you mention a use-case for it, it may propose code like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "93fa311a-7345-4e02-9466-beea1ca3cd68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <div style=\"font-size:7pt\">\n",
       "            This notebook may contain text, code and images generated by artificial intelligence.\n",
       "                        \n",
       "            Used model: claude-3-5-sonnet-20240620,\n",
       "            vision model: gpt-4o-2024-05-13,\n",
       "            endpoint: None,\n",
       "            bia-bob version: 0.19.0.\n",
       "            It is good scientific practice to check the code and results it produces carefully.\n",
       "            \n",
       "            <a href=\"https://github.com/haesleinhuepf/bia-bob\">Read more about code generation using bia-bob</a>.\n",
       "            </div>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "I will create two connected scatterplots side-by-side using the penguins_df DataFrame, allowing for interactive selection between the plots.\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%bob display the penguins_df in two connected scatterplots side-by-side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3e2ba589-83e8-43f1-a144-7855d8910579",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4601b43a9ebb43cebace4733a7280694",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HBox(children=(VBox(children=(VBox(children=(HBox(children=(Label(value='Axes '), Dropdown(inde…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "from ipywidgets import HBox\n",
    "import stackview\n",
    "\n",
    "def update2(e=None):\n",
    "    widget2.update()\n",
    "\n",
    "def update1(e=None):\n",
    "    widget1.update()\n",
    "\n",
    "widget1 = stackview.scatterplot(df=penguins_df, column_x=\"bill_length_mm\", column_y=\"bill_depth_mm\", selection_changed_callback=update2)\n",
    "widget2 = stackview.scatterplot(df=penguins_df, column_x=\"flipper_length_mm\", column_y=\"body_mass_g\", selection_changed_callback=update1)\n",
    "\n",
    "display(HBox([widget1, widget2])) # Arrange the widgets side by side using HBox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cd91c32-9eea-44f6-a13c-5093b55ccfa1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}