notebooks/01-use-cases/other-industries/burritos/main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "005aa705-29f4-463b-9b5a-e39f615c0654",
   "metadata": {},
   "source": [
    "\\[_In case you’re unable to see the Atoti visualizations in GitHub, try viewing the notebook in [nbviewer](https://nbviewer.org/github/atoti/notebooks/blob/main/notebooks/01-use-cases/other-industries/burritos/main.ipynb)._]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f443131b-51a9-4d8b-bc83-063a645df312",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import relevant libraries\n",
    "import re\n",
    "\n",
    "import atoti as tt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "83e8778f-664c-4e2a-87a7-e8637e047407",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load in data\n",
    "df = pd.read_csv(\"./data/burritos_01022018.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a1c3c63e-c78e-4002-849c-930566a6c94c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Location', 'Burrito', 'Date', 'Neighborhood', 'Address', 'URL', 'Yelp',\n",
       "       'Google', 'Chips', 'Cost', 'Hunger', 'Mass (g)', 'Density (g/mL)',\n",
       "       'Length', 'Circum', 'Volume', 'Tortilla', 'Temp', 'Meat', 'Fillings',\n",
       "       'Meat:filling', 'Uniformity', 'Salsa', 'Synergy', 'Wrap', 'overall',\n",
       "       'Rec', 'Reviewer', 'Notes', 'Unreliable', 'NonSD', 'Beef', 'Pico',\n",
       "       'Guac', 'Cheese', 'Fries', 'Sour cream', 'Pork', 'Chicken', 'Shrimp',\n",
       "       'Fish', 'Rice', 'Beans', 'Lettuce', 'Tomato', 'Bell peper', 'Carrots',\n",
       "       'Cabbage', 'Sauce', 'Salsa.1', 'Cilantro', 'Onion', 'Taquito',\n",
       "       'Pineapple', 'Ham', 'Chile relleno', 'Nopales', 'Lobster', 'Queso',\n",
       "       'Egg', 'Mushroom', 'Bacon', 'Sushi', 'Avocado', 'Corn', 'Zucchini'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# explore column headers\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "70cd32d9-9e67-446b-8c99-0581729a413c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Burrito</th>\n",
       "      <th>Date</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Address</th>\n",
       "      <th>URL</th>\n",
       "      <th>Yelp</th>\n",
       "      <th>Google</th>\n",
       "      <th>Chips</th>\n",
       "      <th>Cost</th>\n",
       "      <th>...</th>\n",
       "      <th>Nopales</th>\n",
       "      <th>Lobster</th>\n",
       "      <th>Queso</th>\n",
       "      <th>Egg</th>\n",
       "      <th>Mushroom</th>\n",
       "      <th>Bacon</th>\n",
       "      <th>Sushi</th>\n",
       "      <th>Avocado</th>\n",
       "      <th>Corn</th>\n",
       "      <th>Zucchini</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Donato's taco shop</td>\n",
       "      <td>California</td>\n",
       "      <td>1/18/2016</td>\n",
       "      <td>Miramar</td>\n",
       "      <td>6780 Miramar Rd</td>\n",
       "      <td>http://donatostacoshop.net/</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.49</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>1</th>\n",
       "      <td>Oscar's Mexican food</td>\n",
       "      <td>California</td>\n",
       "      <td>1/24/2016</td>\n",
       "      <td>San Marcos</td>\n",
       "      <td>225 S Rancho Santa Fe Rd</td>\n",
       "      <td>http://www.yelp.com/biz/oscars-mexican-food-sa...</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.45</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>2</th>\n",
       "      <td>Oscar's Mexican food</td>\n",
       "      <td>Carnitas</td>\n",
       "      <td>1/24/2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.85</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>3</th>\n",
       "      <td>Oscar's Mexican food</td>\n",
       "      <td>Carne asada</td>\n",
       "      <td>1/24/2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.25</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>Pollos Maria</td>\n",
       "      <td>California</td>\n",
       "      <td>1/27/2016</td>\n",
       "      <td>Carlsbad</td>\n",
       "      <td>3055 Harding St</td>\n",
       "      <td>http://pollosmaria.com/</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>x</td>\n",
       "      <td>6.59</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Location      Burrito       Date Neighborhood  \\\n",
       "0    Donato's taco shop  California   1/18/2016      Miramar   \n",
       "1  Oscar's Mexican food  California   1/24/2016   San Marcos   \n",
       "2  Oscar's Mexican food     Carnitas  1/24/2016          NaN   \n",
       "3  Oscar's Mexican food  Carne asada  1/24/2016          NaN   \n",
       "4          Pollos Maria   California  1/27/2016     Carlsbad   \n",
       "\n",
       "                    Address  \\\n",
       "0           6780 Miramar Rd   \n",
       "1  225 S Rancho Santa Fe Rd   \n",
       "2                       NaN   \n",
       "3                       NaN   \n",
       "4           3055 Harding St   \n",
       "\n",
       "                                                 URL  Yelp  Google Chips  \\\n",
       "0                        http://donatostacoshop.net/   3.5     4.2   NaN   \n",
       "1  http://www.yelp.com/biz/oscars-mexican-food-sa...   3.5     3.3   NaN   \n",
       "2                                                NaN   NaN     NaN   NaN   \n",
       "3                                                NaN   NaN     NaN   NaN   \n",
       "4                            http://pollosmaria.com/   4.0     3.8     x   \n",
       "\n",
       "   Cost  ...  Nopales  Lobster  Queso  Egg  Mushroom  Bacon  Sushi  Avocado  \\\n",
       "0  6.49  ...      NaN      NaN    NaN  NaN       NaN    NaN    NaN      NaN   \n",
       "1  5.45  ...      NaN      NaN    NaN  NaN       NaN    NaN    NaN      NaN   \n",
       "2  4.85  ...      NaN      NaN    NaN  NaN       NaN    NaN    NaN      NaN   \n",
       "3  5.25  ...      NaN      NaN    NaN  NaN       NaN    NaN    NaN      NaN   \n",
       "4  6.59  ...      NaN      NaN    NaN  NaN       NaN    NaN    NaN      NaN   \n",
       "\n",
       "   Corn  Zucchini  \n",
       "0   NaN       NaN  \n",
       "1   NaN       NaN  \n",
       "2   NaN       NaN  \n",
       "3   NaN       NaN  \n",
       "4   NaN       NaN  \n",
       "\n",
       "[5 rows x 66 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at first 5 rows of data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "36bc2858-d5aa-4383-9993-5bb2f99c96bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yelp</th>\n",
       "      <th>Google</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Hunger</th>\n",
       "      <th>Mass (g)</th>\n",
       "      <th>Density (g/mL)</th>\n",
       "      <th>Length</th>\n",
       "      <th>Circum</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Tortilla</th>\n",
       "      <th>Temp</th>\n",
       "      <th>Meat</th>\n",
       "      <th>Fillings</th>\n",
       "      <th>Meat:filling</th>\n",
       "      <th>Uniformity</th>\n",
       "      <th>Salsa</th>\n",
       "      <th>Synergy</th>\n",
       "      <th>Wrap</th>\n",
       "      <th>overall</th>\n",
       "      <th>Queso</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>82.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>378.000000</td>\n",
       "      <td>382.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>251.000000</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>385.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>383.000000</td>\n",
       "      <td>377.000000</td>\n",
       "      <td>383.000000</td>\n",
       "      <td>363.000000</td>\n",
       "      <td>383.000000</td>\n",
       "      <td>383.000000</td>\n",
       "      <td>383.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.898780</td>\n",
       "      <td>4.174390</td>\n",
       "      <td>7.048280</td>\n",
       "      <td>3.499895</td>\n",
       "      <td>546.181818</td>\n",
       "      <td>0.675277</td>\n",
       "      <td>20.072988</td>\n",
       "      <td>22.098996</td>\n",
       "      <td>0.785462</td>\n",
       "      <td>3.486104</td>\n",
       "      <td>3.741096</td>\n",
       "      <td>3.596247</td>\n",
       "      <td>3.527546</td>\n",
       "      <td>3.564403</td>\n",
       "      <td>3.422324</td>\n",
       "      <td>3.348485</td>\n",
       "      <td>3.576371</td>\n",
       "      <td>3.995561</td>\n",
       "      <td>3.604813</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.470748</td>\n",
       "      <td>0.377389</td>\n",
       "      <td>1.517983</td>\n",
       "      <td>0.808791</td>\n",
       "      <td>144.445619</td>\n",
       "      <td>0.080468</td>\n",
       "      <td>2.060584</td>\n",
       "      <td>1.795010</td>\n",
       "      <td>0.153465</td>\n",
       "      <td>0.787282</td>\n",
       "      <td>0.975079</td>\n",
       "      <td>0.835896</td>\n",
       "      <td>0.812342</td>\n",
       "      <td>0.987858</td>\n",
       "      <td>1.061032</td>\n",
       "      <td>0.927714</td>\n",
       "      <td>0.896275</td>\n",
       "      <td>1.107876</td>\n",
       "      <td>0.761901</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>2.990000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>450.000000</td>\n",
       "      <td>0.619485</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>540.000000</td>\n",
       "      <td>0.658099</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>7.750000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>595.000000</td>\n",
       "      <td>0.721726</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.880000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>925.000000</td>\n",
       "      <td>0.865672</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1.540000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Yelp     Google        Cost      Hunger    Mass (g)  \\\n",
       "count  82.000000  82.000000  378.000000  382.000000   22.000000   \n",
       "mean    3.898780   4.174390    7.048280    3.499895  546.181818   \n",
       "std     0.470748   0.377389    1.517983    0.808791  144.445619   \n",
       "min     2.500000   2.900000    2.990000    0.500000  350.000000   \n",
       "25%     3.500000   4.000000    6.250000    3.000000  450.000000   \n",
       "50%     4.000000   4.200000    6.950000    3.500000  540.000000   \n",
       "75%     4.000000   4.400000    7.750000    4.000000  595.000000   \n",
       "max     4.500000   5.000000   25.000000    5.000000  925.000000   \n",
       "\n",
       "       Density (g/mL)      Length      Circum      Volume    Tortilla  \\\n",
       "count       22.000000  251.000000  249.000000  249.000000  385.000000   \n",
       "mean         0.675277   20.072988   22.098996    0.785462    3.486104   \n",
       "std          0.080468    2.060584    1.795010    0.153465    0.787282   \n",
       "min          0.560000   15.000000   17.000000    0.400000    1.000000   \n",
       "25%          0.619485   18.500000   21.000000    0.680000    3.000000   \n",
       "50%          0.658099   20.000000   22.000000    0.770000    3.500000   \n",
       "75%          0.721726   21.500000   23.000000    0.880000    4.000000   \n",
       "max          0.865672   26.000000   29.000000    1.540000    5.000000   \n",
       "\n",
       "             Temp        Meat    Fillings  Meat:filling  Uniformity  \\\n",
       "count  365.000000  373.000000  383.000000    377.000000  383.000000   \n",
       "mean     3.741096    3.596247    3.527546      3.564403    3.422324   \n",
       "std      0.975079    0.835896    0.812342      0.987858    1.061032   \n",
       "min      1.000000    1.000000    1.000000      0.500000    0.000000   \n",
       "25%      3.000000    3.000000    3.000000      3.000000    2.500000   \n",
       "50%      4.000000    3.750000    3.500000      4.000000    3.500000   \n",
       "75%      4.500000    4.000000    4.000000      4.000000    4.000000   \n",
       "max      5.000000    5.000000    5.000000      5.000000    5.000000   \n",
       "\n",
       "            Salsa     Synergy        Wrap     overall  Queso  \n",
       "count  363.000000  383.000000  383.000000  383.000000    0.0  \n",
       "mean     3.348485    3.576371    3.995561    3.604813    NaN  \n",
       "std      0.927714    0.896275    1.107876    0.761901    NaN  \n",
       "min      0.000000    1.000000    0.000000    1.000000    NaN  \n",
       "25%      3.000000    3.000000    3.500000    3.000000    NaN  \n",
       "50%      3.500000    3.800000    4.000000    3.750000    NaN  \n",
       "75%      4.000000    4.000000    5.000000    4.100000    NaN  \n",
       "max      5.000000    5.000000    5.000000    5.000000    NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# explore shape and central tendency\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dabcd851-8b66-40e5-aa08-3091eceb0393",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use regular expression to remove () from columns\n",
    "# use .replace() to remove : from columns\n",
    "df.columns = [re.sub(\"([\\(\\[]).*?([\\)\\]])\", \"\", x).strip() for x in df.columns]\n",
    "df.columns = [x.replace(\":\", \"_\").strip() for x in df.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "205b32a4-a702-4eb0-9f37-031020713a8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Location', 'Burrito', 'Date', 'Neighborhood', 'Address', 'URL', 'Yelp',\n",
       "       'Google', 'Chips', 'Cost', 'Hunger', 'Mass', 'Density', 'Length',\n",
       "       'Circum', 'Volume', 'Tortilla', 'Temp', 'Meat', 'Fillings',\n",
       "       'Meat_filling', 'Uniformity', 'Salsa', 'Synergy', 'Wrap', 'overall',\n",
       "       'Rec', 'Reviewer', 'Notes', 'Unreliable', 'NonSD', 'Beef', 'Pico',\n",
       "       'Guac', 'Cheese', 'Fries', 'Sour cream', 'Pork', 'Chicken', 'Shrimp',\n",
       "       'Fish', 'Rice', 'Beans', 'Lettuce', 'Tomato', 'Bell peper', 'Carrots',\n",
       "       'Cabbage', 'Sauce', 'Salsa.1', 'Cilantro', 'Onion', 'Taquito',\n",
       "       'Pineapple', 'Ham', 'Chile relleno', 'Nopales', 'Lobster', 'Queso',\n",
       "       'Egg', 'Mushroom', 'Bacon', 'Sushi', 'Avocado', 'Corn', 'Zucchini'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8b12b322-ba65-4c45-b1c5-788c285cff37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location        0.000000\n",
       "Burrito         0.000000\n",
       "Date            0.000000\n",
       "Neighborhood    0.774026\n",
       "Address         0.784416\n",
       "                  ...   \n",
       "Bacon           0.992208\n",
       "Sushi           0.994805\n",
       "Avocado         0.966234\n",
       "Corn            0.992208\n",
       "Zucchini        0.997403\n",
       "Length: 66, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check if there are nulls in column\n",
    "df.isnull().any()\n",
    "# check the % of the column that is null\n",
    "df.isnull().sum() / df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4e848027-2d04-4939-a0cf-27f9b6545b79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create an atoti session and store dashboard files in ./content\n",
    "session = tt.Session(\n",
    "    port=9000,\n",
    "    user_content_storage=\"./content\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "81b60120-7baf-4795-b60c-508219cb888d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scale circumference, volume, fillings, length, mass, and cost data\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "burrito_vars_norm = df.loc[:, [\"Circum\", \"Volume\", \"Length\", \"Mass\", \"Cost\"]]\n",
    "\n",
    "# change 0-1 scaline to 0-10 scale for readability\n",
    "bnorms = scaler.fit_transform(burrito_vars_norm) * 10\n",
    "\n",
    "# create new columns for normalized values in our datframe (df)\n",
    "df[[\"Circum_norm\", \"Volume_norm\", \"Length_norm\", \"Mass_norm\", \"Cost_norm\"]] = bnorms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2effd92a-09b9-420c-8675-6c8afbb2775f",
   "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>Location</th>\n",
       "      <th>Burrito</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Donato's taco shop</td>\n",
       "      <td>California</td>\n",
       "      <td>Circum_norm</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Oscar's Mexican food</td>\n",
       "      <td>California</td>\n",
       "      <td>Circum_norm</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Oscar's Mexican food</td>\n",
       "      <td>Carnitas</td>\n",
       "      <td>Circum_norm</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Oscar's Mexican food</td>\n",
       "      <td>Carne asada</td>\n",
       "      <td>Circum_norm</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pollos Maria</td>\n",
       "      <td>California</td>\n",
       "      <td>Circum_norm</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>Rigoberto's Taco Shop</td>\n",
       "      <td>California</td>\n",
       "      <td>Cost_norm</td>\n",
       "      <td>1.753748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1921</th>\n",
       "      <td>Rigoberto's Taco Shop</td>\n",
       "      <td>California</td>\n",
       "      <td>Cost_norm</td>\n",
       "      <td>1.753748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1922</th>\n",
       "      <td>Burrito Box</td>\n",
       "      <td>Steak with guacamole</td>\n",
       "      <td>Cost_norm</td>\n",
       "      <td>3.866424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1923</th>\n",
       "      <td>Taco Stand</td>\n",
       "      <td>California</td>\n",
       "      <td>Cost_norm</td>\n",
       "      <td>2.226261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td>Taco Stand</td>\n",
       "      <td>California</td>\n",
       "      <td>Cost_norm</td>\n",
       "      <td>2.226261</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1925 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Location               Burrito     variable     value\n",
       "0        Donato's taco shop           California   Circum_norm       NaN\n",
       "1      Oscar's Mexican food           California   Circum_norm       NaN\n",
       "2      Oscar's Mexican food              Carnitas  Circum_norm       NaN\n",
       "3      Oscar's Mexican food           Carne asada  Circum_norm       NaN\n",
       "4              Pollos Maria            California  Circum_norm       NaN\n",
       "...                     ...                   ...          ...       ...\n",
       "1920  Rigoberto's Taco Shop           California     Cost_norm  1.753748\n",
       "1921  Rigoberto's Taco Shop           California     Cost_norm  1.753748\n",
       "1922            Burrito Box  Steak with guacamole    Cost_norm  3.866424\n",
       "1923             Taco Stand           California     Cost_norm  2.226261\n",
       "1924             Taco Stand           California     Cost_norm  2.226261\n",
       "\n",
       "[1925 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create new dataframe for variables so they can be used in a visualization platform\n",
    "# melt reshapes the data so that we have 1 column with circum, volume, length, mass, and cost as features & 1 with values.\n",
    "# we increaste the number of rows by 5x and decrease the number of columns by 4.\n",
    "burrito_variables = pd.melt(\n",
    "    df.reset_index(),\n",
    "    id_vars=[\"Location\", \"Burrito\"],\n",
    "    value_vars=[\"Circum_norm\", \"Volume_norm\", \"Length_norm\", \"Mass_norm\", \"Cost_norm\"],\n",
    ")\n",
    "burrito_variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e465049e-f10a-4525-8e57-740a6d79b8c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# upload dataframe to atoti session\n",
    "burrito_table = session.read_pandas(df, table_name=\"burritos\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "353825fc-cec7-4754-a71d-c3701f339cf6",
   "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>Location</th>\n",
       "      <th>Burrito</th>\n",
       "      <th>Date</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Address</th>\n",
       "      <th>URL</th>\n",
       "      <th>Yelp</th>\n",
       "      <th>Google</th>\n",
       "      <th>Chips</th>\n",
       "      <th>Cost</th>\n",
       "      <th>...</th>\n",
       "      <th>Bacon</th>\n",
       "      <th>Sushi</th>\n",
       "      <th>Avocado</th>\n",
       "      <th>Corn</th>\n",
       "      <th>Zucchini</th>\n",
       "      <th>Circum_norm</th>\n",
       "      <th>Volume_norm</th>\n",
       "      <th>Length_norm</th>\n",
       "      <th>Mass_norm</th>\n",
       "      <th>Cost_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Donato's taco shop</td>\n",
       "      <td>California</td>\n",
       "      <td>1/18/2016</td>\n",
       "      <td>Miramar</td>\n",
       "      <td>6780 Miramar Rd</td>\n",
       "      <td>http://donatostacoshop.net/</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>N/A</td>\n",
       "      <td>6.49</td>\n",
       "      <td>...</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.590186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nico's Taco Shop</td>\n",
       "      <td>Carnitas</td>\n",
       "      <td>1/30/2016</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N/A</td>\n",
       "      <td>6.99</td>\n",
       "      <td>...</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.817356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>La Perla Cocina</td>\n",
       "      <td>Oaxacalifornia</td>\n",
       "      <td>3/30/2016</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N/A</td>\n",
       "      <td>9.99</td>\n",
       "      <td>...</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.180373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rigoberto's Taco Shop</td>\n",
       "      <td>Carne asada</td>\n",
       "      <td>5/13/2016</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N/A</td>\n",
       "      <td>6.60</td>\n",
       "      <td>...</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>4.166667</td>\n",
       "      <td>3.947368</td>\n",
       "      <td>6.363636</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.640164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rigoberto's Taco Shop</td>\n",
       "      <td>Carnitas</td>\n",
       "      <td>5/13/2016</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N/A</td>\n",
       "      <td>7.50</td>\n",
       "      <td>...</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>4.583333</td>\n",
       "      <td>4.824561</td>\n",
       "      <td>7.727273</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.049069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Location         Burrito       Date Neighborhood  \\\n",
       "0     Donato's taco shop     California   1/18/2016      Miramar   \n",
       "1       Nico's Taco Shop        Carnitas  1/30/2016          N/A   \n",
       "2        La Perla Cocina  Oaxacalifornia  3/30/2016          N/A   \n",
       "3  Rigoberto's Taco Shop     Carne asada  5/13/2016          N/A   \n",
       "4  Rigoberto's Taco Shop        Carnitas  5/13/2016          N/A   \n",
       "\n",
       "           Address                          URL  Yelp  Google Chips  Cost  \\\n",
       "0  6780 Miramar Rd  http://donatostacoshop.net/   3.5     4.2   N/A  6.49   \n",
       "1              N/A                          N/A   NaN     NaN   N/A  6.99   \n",
       "2              N/A                          N/A   NaN     NaN   N/A  9.99   \n",
       "3              N/A                          N/A   NaN     NaN   N/A  6.60   \n",
       "4              N/A                          N/A   NaN     NaN   N/A  7.50   \n",
       "\n",
       "   ...  Bacon  Sushi  Avocado  Corn  Zucchini  Circum_norm  Volume_norm  \\\n",
       "0  ...    N/A    N/A      N/A   N/A       N/A          NaN          NaN   \n",
       "1  ...    N/A    N/A      N/A   N/A       N/A          NaN          NaN   \n",
       "2  ...    N/A    N/A      N/A   N/A       N/A          NaN          NaN   \n",
       "3  ...    N/A    N/A      N/A   N/A       N/A     4.166667     3.947368   \n",
       "4  ...    N/A    N/A      N/A   N/A       N/A     4.583333     4.824561   \n",
       "\n",
       "   Length_norm  Mass_norm  Cost_norm  \n",
       "0          NaN        NaN   1.590186  \n",
       "1          NaN        NaN   1.817356  \n",
       "2          NaN        NaN   3.180373  \n",
       "3     6.363636        NaN   1.640164  \n",
       "4     7.727273        NaN   2.049069  \n",
       "\n",
       "[5 rows x 71 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "burrito_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e2bb40f4-f5c9-42c7-84a8-dbe441972a38",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create data cube\n",
    "cube = session.create_cube(burrito_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9203b3f4-353a-48b4-8677-d6f8493198e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create hierarchies, levels, and measures\n",
    "h = cube.hierarchies\n",
    "l = cube.levels\n",
    "m = cube.measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8ef8448e-4979-4c55-adbf-80921a75aef6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create new measures (examples)\n",
    "m[\"five\"] = 5\n",
    "m[\"lenXwrap\"] = m[\"Length.MEAN\"] * m[\"Wrap.MEAN\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7dd10de6-e2d2-4b3e-a878-a4ca374c12f5",
   "metadata": {
    "atoti": {
     "widget": {
      "columnWidths": {
       "[burritos].[Location].[Location]": 181.22915649414062
      },
      "mapping": {
       "columns": [
        "ALL_MEASURES"
       ],
       "measures": [
        "[Measures].[five]",
        "[Measures].[Length.MEAN]",
        "[Measures].[lenXwrap]"
       ],
       "rows": [
        "[burritos].[Location].[Location]"
       ]
      },
      "query": {
       "mdx": "SELECT NON EMPTY Order(Hierarchize(Descendants({[burritos].[Location].[AllMember]}, 1, SELF_AND_BEFORE)), [Measures].[lenXwrap], DESC) ON ROWS, NON EMPTY {[Measures].[five], [Measures].[Length.MEAN], [Measures].[lenXwrap]} ON COLUMNS FROM [burritos] CELL PROPERTIES VALUE, FORMATTED_VALUE, BACK_COLOR, FORE_COLOR, FONT_FLAGS",
       "updateMode": "once"
      },
      "serverKey": "default",
      "widgetKey": "pivot-table"
     }
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.atoti.widget.v0+json": {
       "name": "exploration 1",
       "sessionId": "1685408990_FTRSEX",
       "sessionLocation": {
        "https": false,
        "port": 9000
       },
       "widgetCreationCode": "session.visualize()"
      },
      "text/html": [
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      ],
      "text/plain": [
       "Open the notebook in JupyterLab with the Atoti extension enabled to build this widget."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create pivot table visual / example\n",
    "session.visualize(\"exploration 1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d7dcbdbf-f984-4f92-b035-0610462148bc",
   "metadata": {
    "atoti": {
     "widget": {
      "mapping": {
       "color": [
        "[burritos].[Neighborhood].[Neighborhood]"
       ],
       "horizontalSubplots": [],
       "size": [],
       "splitBy": [
        "[burritos].[Location].[Location]"
       ],
       "verticalSubplots": [],
       "xValues": [
        "[Measures].[Yelp.MEAN]"
       ],
       "yValues": [
        "[Measures].[Google.MEAN]"
       ]
      },
      "name": "scatter plot",
      "query": {
       "mdx": "SELECT NON EMPTY {[Measures].[Yelp.MEAN], [Measures].[Google.MEAN]} ON COLUMNS, NON EMPTY Crossjoin(Hierarchize(Descendants({[burritos].[Neighborhood].[AllMember]}, 1, SELF_AND_BEFORE)), Hierarchize(Descendants({[burritos].[Location].[AllMember]}, 1, SELF_AND_BEFORE))) ON ROWS FROM [burritos] CELL PROPERTIES VALUE, FORMATTED_VALUE, BACK_COLOR, FORE_COLOR, FONT_FLAGS",
       "updateMode": "once"
      },
      "serverKey": "default",
      "widgetKey": "plotly-scatter-plot"
     }
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.atoti.widget.v0+json": {
       "name": "scatter plot neighborhood yelp google",
       "sessionId": "1685408990_FTRSEX",
       "sessionLocation": {
        "https": false,
        "port": 9000
       },
       "widgetCreationCode": "session.visualize()"
      },
      "text/html": [
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      ],
      "text/plain": [
       "Open the notebook in JupyterLab with the Atoti extension enabled to build this widget."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create scatterplot visual /example\n",
    "session.visualize(\"scatter plot neighborhood yelp google\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "214dd5de-234f-4a64-907b-24f973c2166b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add reformatted data to table to visualize\n",
    "burrito_var_table = session.read_pandas(\n",
    "    burrito_variables,\n",
    "    table_name=\"burrito_variables\",\n",
    "    keys=[\"Location\", \"Burrito\", \"variable\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "11650af9-58a2-4126-a746-cd6a475766e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# join main dataframe with new burrito_var_table\n",
    "burrito_table.join(burrito_var_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bd1dd3ce-174f-4237-a1ea-5d93c50f7bf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create value measure in atoti with values for each of the burrito variables that we made above (mass, circum, cost, etc.)\n",
    "m[\"value\"] = tt.agg.mean(burrito_var_table[\"value\"])\n",
    "m[\"aggvalue\"] = tt.agg.mean(\n",
    "    m[\"value\"], scope=tt.OriginScope(l[\"Location\"], l[\"Burrito\"], l[\"variable\"])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9ed85dd1-d103-4d7c-8db7-871d218589cf",
   "metadata": {
    "atoti": {
     "widget": {
      "mapping": {
       "axis": [
        "[burrito_variables].[variable].[variable]"
       ],
       "horizontalSubplots": [
        "[burritos].[Location].[Location]"
       ],
       "splitBy": [
        "ALL_MEASURES"
       ],
       "values": [
        "[Measures].[value]"
       ],
       "verticalSubplots": []
      },
      "name": "radar chart final",
      "query": {
       "mdx": "SELECT NON EMPTY Crossjoin(Hierarchize(Descendants({[burritos].[Location].[AllMember]}, 1, SELF_AND_BEFORE)), {[Measures].[value]}) ON COLUMNS, NON EMPTY Hierarchize(Descendants({[burrito_variables].[variable].[AllMember]}, 1, SELF_AND_BEFORE)) ON ROWS FROM [burritos] CELL PROPERTIES VALUE, FORMATTED_VALUE, BACK_COLOR, FORE_COLOR, FONT_FLAGS",
       "updateMode": "once"
      },
      "serverKey": "default",
      "widgetKey": "plotly-radar-chart"
     }
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.atoti.widget.v0+json": {
       "name": "radar chart final",
       "sessionId": "1685408990_FTRSEX",
       "sessionLocation": {
        "https": false,
        "port": 9000
       },
       "widgetCreationCode": "session.visualize()"
      },
      "text/html": [
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      ],
      "text/plain": [
       "Open the notebook in JupyterLab with the Atoti extension enabled to build this widget."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create radar chart\n",
    "session.visualize(\"radar chart final\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e53245d3-1848-45ed-a76f-044325cd900d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# new data for yelp gauge\n",
    "m[\"four\"] = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4205eada-1e22-4b0a-8666-f6e463d08bb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# new data for google gauge\n",
    "m[\"four_google\"] = 4.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7863b9ea-0b25-4940-9cd5-1b123c84fa7b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.atoti.link.v0+json": {
       "path": "#/dashboard/1be",
       "sessionLocation": {
        "https": false,
        "port": 9000
       }
      },
      "text/plain": [
       "Open the notebook in JupyterLab with the Atoti extension enabled to see this link."
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.link(path=\"/#/dashboard/1be\")"
   ]
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}