notebooks/wine-analytics/main.ipynb

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   "outputs": [],
   "source": [
    "import atoti as tt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c836de6a-6dda-4e43-8e2b-49f1453cc277",
   "metadata": {},
   "source": [
    "### Reading data for red and white wine\n",
    "\n",
    "In this notebook, we are using the wine quality datasets from https://archive.ics.uci.edu/ml/datasets/Wine+Quality. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fccf67ae-42b1-4743-99ef-b7526422b5a8",
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       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
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       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
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       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
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       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
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       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
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       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
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       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
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     "execution_count": 2,
     "metadata": {},
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   ],
   "source": [
    "wine_red = pd.read_csv(\n",
    "    \"https://data.atoti.io/notebooks/wine-analytics/winequality-red.csv\", sep=\";\"\n",
    ")\n",
    "wine_red.head()"
   ]
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   "execution_count": 3,
   "id": "8b950aee-19c8-45c9-9358-22c240d7ff87",
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       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
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       "      <th>quality</th>\n",
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       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
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       "      <td>6</td>\n",
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       "      <th>1</th>\n",
       "      <td>6.3</td>\n",
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       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
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       "      <th>4</th>\n",
       "      <td>7.2</td>\n",
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       "      <td>8.5</td>\n",
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       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
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       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "3            7.2              0.23         0.32             8.5      0.058   \n",
       "4            7.2              0.23         0.32             8.5      0.058   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
       "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
       "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
       "3                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "4                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      8.8        6  \n",
       "1      9.5        6  \n",
       "2     10.1        6  \n",
       "3      9.9        6  \n",
       "4      9.9        6  "
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "wine_white = pd.read_csv(\n",
    "    \"https://data.atoti.io/notebooks/wine-analytics/winequality-white.csv\", sep=\";\"\n",
    ")\n",
    "wine_white.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fa1800d-b87d-48d6-8189-4fd121aee782",
   "metadata": {},
   "source": [
    "### Merging dataset for loading into atoti\n",
    "\n",
    "We can combine the 2 dataset by adding the category for the wine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e2374fa2-d33c-4e0e-a451-91f1e31cc042",
   "metadata": {},
   "outputs": [
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       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
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       "      <th>category</th>\n",
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       "    <tr>\n",
       "      <th>wine index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "      <td>Red</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
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       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "      <td>Red</td>\n",
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       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
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       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
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       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "      <td>Red</td>\n",
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      "text/plain": [
       "            fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "wine index                                                                 \n",
       "0                     7.4              0.70         0.00             1.9   \n",
       "1                     7.8              0.88         0.00             2.6   \n",
       "2                     7.8              0.76         0.04             2.3   \n",
       "3                    11.2              0.28         0.56             1.9   \n",
       "4                     7.4              0.70         0.00             1.9   \n",
       "\n",
       "            chlorides  free sulfur dioxide  total sulfur dioxide  density  \\\n",
       "wine index                                                                  \n",
       "0               0.076                 11.0                  34.0   0.9978   \n",
       "1               0.098                 25.0                  67.0   0.9968   \n",
       "2               0.092                 15.0                  54.0   0.9970   \n",
       "3               0.075                 17.0                  60.0   0.9980   \n",
       "4               0.076                 11.0                  34.0   0.9978   \n",
       "\n",
       "              pH  sulphates  alcohol  quality category  \n",
       "wine index                                              \n",
       "0           3.51       0.56      9.4        5      Red  \n",
       "1           3.20       0.68      9.8        5      Red  \n",
       "2           3.26       0.65      9.8        5      Red  \n",
       "3           3.16       0.58      9.8        6      Red  \n",
       "4           3.51       0.56      9.4        5      Red  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_red[\"category\"] = \"Red\"\n",
    "wine_white[\"category\"] = \"White\"\n",
    "\n",
    "wines = pd.concat([wine_red, wine_white], axis=0, ignore_index=True)\n",
    "wines.index.set_names(\"wine index\", inplace=True)\n",
    "wines.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b97a84cf-bbcc-4b46-8f9c-bcc659454da8",
   "metadata": {},
   "source": [
    "We group the wines into 3 broad ratings-good, average and poor-based on the quality. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e2fa35f1-5c59-41b4-9d21-129f14351523",
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>wine index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.99780</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "      <td>Red</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.99680</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "      <td>Red</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.99700</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "      <td>Red</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.99800</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "      <td>Red</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.99780</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "      <td>Red</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6492</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.039</td>\n",
       "      <td>24.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.99114</td>\n",
       "      <td>3.27</td>\n",
       "      <td>0.50</td>\n",
       "      <td>11.2</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6493</th>\n",
       "      <td>6.6</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.36</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.047</td>\n",
       "      <td>57.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.99490</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.6</td>\n",
       "      <td>5</td>\n",
       "      <td>White</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6494</th>\n",
       "      <td>6.5</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.041</td>\n",
       "      <td>30.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>0.99254</td>\n",
       "      <td>2.99</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.4</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6495</th>\n",
       "      <td>5.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.022</td>\n",
       "      <td>20.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.98869</td>\n",
       "      <td>3.34</td>\n",
       "      <td>0.38</td>\n",
       "      <td>12.8</td>\n",
       "      <td>7</td>\n",
       "      <td>White</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6496</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.020</td>\n",
       "      <td>22.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.98941</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.32</td>\n",
       "      <td>11.8</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6497 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "wine index                                                                 \n",
       "0                     7.4              0.70         0.00             1.9   \n",
       "1                     7.8              0.88         0.00             2.6   \n",
       "2                     7.8              0.76         0.04             2.3   \n",
       "3                    11.2              0.28         0.56             1.9   \n",
       "4                     7.4              0.70         0.00             1.9   \n",
       "...                   ...               ...          ...             ...   \n",
       "6492                  6.2              0.21         0.29             1.6   \n",
       "6493                  6.6              0.32         0.36             8.0   \n",
       "6494                  6.5              0.24         0.19             1.2   \n",
       "6495                  5.5              0.29         0.30             1.1   \n",
       "6496                  6.0              0.21         0.38             0.8   \n",
       "\n",
       "            chlorides  free sulfur dioxide  total sulfur dioxide  density  \\\n",
       "wine index                                                                  \n",
       "0               0.076                 11.0                  34.0  0.99780   \n",
       "1               0.098                 25.0                  67.0  0.99680   \n",
       "2               0.092                 15.0                  54.0  0.99700   \n",
       "3               0.075                 17.0                  60.0  0.99800   \n",
       "4               0.076                 11.0                  34.0  0.99780   \n",
       "...               ...                  ...                   ...      ...   \n",
       "6492            0.039                 24.0                  92.0  0.99114   \n",
       "6493            0.047                 57.0                 168.0  0.99490   \n",
       "6494            0.041                 30.0                 111.0  0.99254   \n",
       "6495            0.022                 20.0                 110.0  0.98869   \n",
       "6496            0.020                 22.0                  98.0  0.98941   \n",
       "\n",
       "              pH  sulphates  alcohol  quality category   rating  \n",
       "wine index                                                       \n",
       "0           3.51       0.56      9.4        5      Red  Average  \n",
       "1           3.20       0.68      9.8        5      Red  Average  \n",
       "2           3.26       0.65      9.8        5      Red  Average  \n",
       "3           3.16       0.58      9.8        6      Red  Average  \n",
       "4           3.51       0.56      9.4        5      Red  Average  \n",
       "...          ...        ...      ...      ...      ...      ...  \n",
       "6492        3.27       0.50     11.2        6    White  Average  \n",
       "6493        3.15       0.46      9.6        5    White  Average  \n",
       "6494        2.99       0.46      9.4        6    White  Average  \n",
       "6495        3.34       0.38     12.8        7    White     Good  \n",
       "6496        3.26       0.32     11.8        6    White  Average  \n",
       "\n",
       "[6497 rows x 14 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wines[\"rating\"] = \"Good\"\n",
    "wines.loc[wines[\"quality\"] < 7, \"rating\"] = \"Average\"\n",
    "wines.loc[wines[\"quality\"] < 5, \"rating\"] = \"Poor\"\n",
    "wines"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85854f5f-c7c5-411e-89d7-e8de46b0d931",
   "metadata": {},
   "source": [
    "### Wine analysis by correlated features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "20fd8892-6a60-47b1-80ee-2a89593690ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_red = wine_red.corr()\n",
    "ax_red = sns.heatmap(\n",
    "    corr_red,\n",
    "    vmin=-1,\n",
    "    vmax=1,\n",
    "    center=0,\n",
    "    cmap=sns.diverging_palette(20, 220, n=200),\n",
    "    square=True,\n",
    ")\n",
    "ax_red.set_xticklabels(\n",
    "    ax_red.get_xticklabels(), rotation=45, horizontalalignment=\"right\"\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a76a3996-a9d1-46af-9b83-47758ada8902",
   "metadata": {},
   "source": [
    "Let's look at the features that are correlated to the quality. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23380716-54a5-4b04-a0d6-ec3babee8166",
   "metadata": {},
   "source": [
    "The correlation value is not very high, so just for demonstrative purpose, we will take the threshold value of 0.25 to select a few key features that are most correlated.  \n",
    "We will not consider the interaction between the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d5929c46-3bc9-468d-9caa-ace1045bd30a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "volatile acidity   -0.390558\n",
       "sulphates           0.251397\n",
       "alcohol             0.476166\n",
       "quality             1.000000\n",
       "Name: quality, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_red[(corr_red[\"quality\"] > 0.25) | (corr_red[\"quality\"] < -0.25)][\"quality\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fe719531-7fee-4604-a820-19bfa5bc319d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "density   -0.307123\n",
       "alcohol    0.435575\n",
       "quality    1.000000\n",
       "Name: quality, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_white = wine_white.corr()\n",
    "corr_white[(corr_white[\"quality\"] > 0.25) | (corr_white[\"quality\"] < -0.25)][\"quality\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "83bbe2b7-dffd-4ce0-bebc-10ea71df4b36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>wine index</th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "      <th>category</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "      <td>Red</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "      <td>Red</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "      <td>Red</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "      <td>Red</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "      <td>Red</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   wine index  fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "0           0            7.4              0.70         0.00             1.9   \n",
       "1           1            7.8              0.88         0.00             2.6   \n",
       "2           2            7.8              0.76         0.04             2.3   \n",
       "3           3           11.2              0.28         0.56             1.9   \n",
       "4           4            7.4              0.70         0.00             1.9   \n",
       "\n",
       "   chlorides  free sulfur dioxide  total sulfur dioxide  density    pH  \\\n",
       "0      0.076                 11.0                  34.0   0.9978  3.51   \n",
       "1      0.098                 25.0                  67.0   0.9968  3.20   \n",
       "2      0.092                 15.0                  54.0   0.9970  3.26   \n",
       "3      0.075                 17.0                  60.0   0.9980  3.16   \n",
       "4      0.076                 11.0                  34.0   0.9978  3.51   \n",
       "\n",
       "   sulphates  alcohol  quality category   rating  \n",
       "0       0.56      9.4        5      Red  Average  \n",
       "1       0.68      9.8        5      Red  Average  \n",
       "2       0.65      9.8        5      Red  Average  \n",
       "3       0.58      9.8        6      Red  Average  \n",
       "4       0.56      9.4        5      Red  Average  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_variables = wines.reset_index()\n",
    "wine_variables.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b982db3a-e088-4aa4-96cc-6bbe7dfd5b94",
   "metadata": {},
   "outputs": [],
   "source": [
    "wine_variables_melt = pd.melt(\n",
    "    wine_variables,\n",
    "    id_vars=[\"wine index\"],\n",
    "    value_vars=[\n",
    "        \"fixed acidity\",\n",
    "        \"volatile acidity\",\n",
    "        \"citric acid\",\n",
    "        \"residual sugar\",\n",
    "        \"chlorides\",\n",
    "        \"free sulfur dioxide\",\n",
    "        \"total sulfur dioxide\",\n",
    "        \"density\",\n",
    "        \"pH\",\n",
    "        \"sulphates\",\n",
    "        \"alcohol\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "efdcffcc-1886-4188-a0eb-67c145b80b04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>wine index</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>fixed acidity</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>fixed acidity</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>fixed acidity</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>fixed acidity</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>fixed acidity</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   wine index       variable  value\n",
       "0           0  fixed acidity    7.4\n",
       "1           1  fixed acidity    7.8\n",
       "2           2  fixed acidity    7.8\n",
       "3           3  fixed acidity   11.2\n",
       "4           4  fixed acidity    7.4"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_variables_melt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce25b102-1013-4146-b781-5f6a59819c86",
   "metadata": {},
   "source": [
    "### Getting started with atoti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "da43281e-f5ff-4749-8f39-964503ad13d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "session = tt.create_session(config={\"user_content_storage\": \"./content\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c0598a75-787b-4928-a073-0631d45267e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "wines_table = session.read_pandas(\n",
    "    wine_variables_melt, table_name=\"Wine quality\", keys=[\"wine index\", \"variable\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6e60e473-80a0-439c-b684-ef0be71cf3cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "cube = session.create_cube(wines_table, \"Wines\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6b64a8e5-5246-4e31-a0d8-3f05f0c8fcad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"172px\" height=\"114px\" viewBox=\"0.00 0.00 172.00 114.00\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1.0 1.0) rotate(0) translate(4 110)\">\n",
       "<title>Wines schema</title>\n",
       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-110 168,-110 168,4 -4,4\"/>\n",
       "<!-- Wine quality -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>Wine quality</title>\n",
       "<text text-anchor=\"start\" x=\"45.5\" y=\"-86.6\" font-family=\"-apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'\" font-weight=\"bold\" font-size=\"13.00\">Wine quality</text>\n",
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       "<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"9,-29 9,-53 40,-53 40,-29 9,-29\"/>\n",
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       "<text text-anchor=\"start\" x=\"45\" y=\"-13.6\" font-family=\"-apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'\" font-size=\"13.00\">value</text>\n",
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       "</g>\n",
       "</g>\n",
       "</svg>"
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      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cube.schema"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "896f1e2f-1222-46d0-b3bb-b411323d5279",
   "metadata": {},
   "source": [
    "### Joining additional tables to the cube"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e22180f8-5645-4271-97a1-e275ab01d6d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "wines_detail = session.read_pandas(\n",
    "    wine_variables[[\"wine index\", \"category\", \"rating\", \"quality\"]],\n",
    "    table_name=\"Wine detail\",\n",
    "    keys=[\"wine index\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9f5c4d7c-3492-46ea-8607-dd64e2d7bf0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "wines_table.join(wines_detail)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "714e5d6a-e168-4fdf-9c23-15fb6b0668b6",
   "metadata": {},
   "outputs": [
    {
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       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"503px\" height=\"138px\" viewBox=\"0.00 0.00 503.00 138.00\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1.0 1.0) rotate(0) translate(4 134)\">\n",
       "<title>Wines schema</title>\n",
       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-134 499,-134 499,4 -4,4\"/>\n",
       "<!-- Wine quality -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>Wine quality</title>\n",
       "<text text-anchor=\"start\" x=\"45.5\" y=\"-98.6\" font-family=\"-apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'\" font-weight=\"bold\" font-size=\"13.00\">Wine quality</text>\n",
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       "<polygon fill=\"none\" stroke=\"black\" points=\"8,-16 8,-114 156,-114 156,-16 8,-16\"/>\n",
       "</g>\n",
       "<!-- Wine detail -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>Wine detail</title>\n",
       "<text text-anchor=\"start\" x=\"382\" y=\"-110.6\" font-family=\"-apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'\" font-weight=\"bold\" font-size=\"13.00\">Wine detail</text>\n",
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       "</g>\n",
       "<!-- Wine quality&#45;&gt;Wine detail -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>Wine quality-&gt;Wine detail</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M164.4,-65C213.02,-65 274.83,-65 324.77,-65\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"324.92,-68.5 334.92,-65 324.92,-61.5 324.92,-68.5\"/>\n",
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       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cube.schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a9428730-78b7-4837-9ec1-9555c90bd443",
   "metadata": {
    "atoti": {
     "height": 434,
     "widget": {
      "mapping": {
       "horizontalSubplots": [],
       "splitBy": [
        "[Wine detail].[rating].[rating]",
        "ALL_MEASURES"
       ],
       "values": [
        "[Measures].[value.MEAN]"
       ],
       "verticalSubplots": [
        "[Wine detail].[category].[category]"
       ],
       "xAxis": [
        "[Wine quality].[variable].[variable]"
       ]
      },
      "query": {
       "mdx": "SELECT NON EMPTY Hierarchize(Descendants({[Wine quality].[variable].[AllMember]}, 1, SELF_AND_BEFORE)) ON ROWS, NON EMPTY Crossjoin(Hierarchize(Descendants({[Wine detail].[rating].[AllMember]}, 1, SELF_AND_BEFORE)), {[Measures].[value.MEAN]}, Hierarchize(Descendants({[Wine detail].[category].[AllMember]}, 1, SELF_AND_BEFORE))) ON COLUMNS FROM [Wines] CELL PROPERTIES VALUE, FORMATTED_VALUE, BACK_COLOR, FORE_COLOR, FONT_FLAGS",
       "updateMode": "once"
      },
      "serverKey": "default",
      "widgetKey": "plotly-clustered-column-chart"
     }
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.atoti.widget.v0+json": {
       "name": "Wine characteristics comparison",
       "sessionId": "8a9b87cb-6269-4eaa-8611-7f16c6585f5e",
       "sessionLocation": {
        "https": false,
        "port": 63906
       },
       "widgetCreationCode": "session.visualize()"
      },
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rial%2C%20sans-serif%3B%20font-size%3A%2012px%3B%20fill%3A%20rgb(89%2C%2089%2C%2089)%3B%20fill-opacity%3A%201%3B%20white-space%3A%20pre%3B%22%3EAverage%3C%2Ftext%3E%3Cg%20class%3D%22layers%22%20style%3D%22opacity%3A%201%3B%22%3E%3Cg%20class%3D%22legendfill%22%2F%3E%3Cg%20class%3D%22legendlines%22%2F%3E%3Cg%20class%3D%22legendsymbols%22%3E%3Cg%20class%3D%22legendpoints%22%3E%3Cpath%20class%3D%22legendundefined%22%20d%3D%22M6%2C6H-6V-6H6Z%22%20transform%3D%22translate(20%2C0)%22%20style%3D%22stroke-width%3A%200px%3B%20fill%3A%20rgb(78%2C%20121%2C%20167)%3B%20fill-opacity%3A%201%3B%22%2F%3E%3C%2Fg%3E%3C%2Fg%3E%3C%2Fg%3E%3Crect%20class%3D%22legendtoggle%22%20pointer-events%3D%22all%22%20x%3D%220%22%20y%3D%22-9.5%22%20width%3D%2288.796875%22%20height%3D%2219%22%20style%3D%22cursor%3A%20pointer%3B%20fill%3A%20rgb(0%2C%200%2C%200)%3B%20fill-opacity%3A%200%3B%22%2F%3E%3C%2Fg%3E%3C%2Fg%3E%3Cg%20class%3D%22groups%22%20transform%3D%22translate(0%2C10)%22%3E%3Cg%20class%3D%22traces%22%20transform%3D%22translate(0%2C33.5)%22%20style%3D%22opacity%3A%201%3B%22%3E%3Ctext%20class%3D%22legendtext%22%20text-anchor%3D%22start%22%20x%3D%2240%22%20y%3D%224.680000000000001%22%20data-unformatted%3D%22Good%22%20data-math%3D%22N%22%20style%3D%22font-family%3A%20%26quot%3BOpen%20Sans%26quot%3B%2C%20verdana%2C%20arial%2C%20sans-serif%3B%20font-size%3A%2012px%3B%20fill%3A%20rgb(89%2C%2089%2C%2089)%3B%20fill-opacity%3A%201%3B%20white-space%3A%20pre%3B%22%3EGood%3C%2Ftext%3E%3Cg%20class%3D%22layers%22%20style%3D%22opacity%3A%201%3B%22%3E%3Cg%20class%3D%22legendfill%22%2F%3E%3Cg%20class%3D%22legendlines%22%2F%3E%3Cg%20class%3D%22legendsymbols%22%3E%3Cg%20class%3D%22legendpoints%22%3E%3Cpath%20class%3D%22legendundefined%22%20d%3D%22M6%2C6H-6V-6H6Z%22%20transform%3D%22translate(20%2C0)%22%20style%3D%22stroke-width%3A%200px%3B%20fill%3A%20rgb(242%2C%20142%2C%2044)%3B%20fill-opacity%3A%201%3B%22%2F%3E%3C%2Fg%3E%3C%2Fg%3E%3C%2Fg%3E%3Crect%20class%3D%22legendtoggle%22%20pointer-events%3D%22all%22%20x%3D%220%22%20y%3D%22-9.5%22%20width%3D%2288.796875%22%20height%3D%2219%22%20style%3D%22cursor%3A%20pointer%3B%20fill%3A%20rgb(0%2C%200%2C%200)%3B%20fill-opacity%3A%200%3B%22%2F%3E%3C%2Fg%3E%3C%2Fg%3E%3Cg%20class%3D%22groups%22%20transform%3D%22translate(0%2C20)%22%3E%3Cg%20class%3D%22traces%22%20transform%3D%22translate(0%2C52.5)%22%20style%3D%22opacity%3A%201%3B%22%3E%3Ctext%20class%3D%22legendtext%22%20text-anchor%3D%22start%22%20x%3D%2240%22%20y%3D%224.680000000000001%22%20data-unformatted%3D%22Poor%22%20data-math%3D%22N%22%20style%3D%22font-family%3A%20%26quot%3BOpen%20Sans%26quot%3B%2C%20verdana%2C%20arial%2C%20sans-serif%3B%20font-size%3A%2012px%3B%20fill%3A%20rgb(89%2C%2089%2C%2089)%3B%20fill-opacity%3A%201%3B%20white-space%3A%20pre%3B%22%3EPoor%3C%2Ftext%3E%3Cg%20class%3D%22layers%22%20style%3D%22opacity%3A%201%3B%22%3E%3Cg%20class%3D%22legendfill%22%2F%3E%3Cg%20class%3D%22legendlines%22%2F%3E%3Cg%20class%3D%22legendsymbols%22%3E%3Cg%20class%3D%22legendpoints%22%3E%3Cpath%20class%3D%22legendundefined%22%20d%3D%22M6%2C6H-6V-6H6Z%22%20transform%3D%22translate(20%2C0)%22%20style%3D%22stroke-width%3A%200px%3B%20fill%3A%20rgb(225%2C%2087%2C%2089)%3B%20fill-opacity%3A%201%3B%22%2F%3E%3C%2Fg%3E%3C%2Fg%3E%3C%2Fg%3E%3Crect%20class%3D%22legendtoggle%22%20pointer-events%3D%22all%22%20x%3D%220%22%20y%3D%22-9.5%22%20width%3D%2288.796875%22%20height%3D%2219%22%20style%3D%22cursor%3A%20pointer%3B%20fill%3A%20rgb(0%2C%200%2C%200)%3B%20fill-opacity%3A%200%3B%22%2F%3E%3C%2Fg%3E%3C%2Fg%3E%3C%2Fg%3E%3Crect%20class%3D%22scrollbar%22%20rx%3D%2220%22%20ry%3D%223%22%20width%3D%220%22%20height%3D%220%22%20style%3D%22fill%3A%20rgb(128%2C%20139%2C%20164)%3B%20fill-opacity%3A%201%3B%22%20x%3D%220%22%20y%3D%220%22%2F%3E%3C%2Fg%3E%3Cg%20class%3D%22g-gtitle%22%2F%3E%3Cg%20class%3D%22g-xtitle%22%2F%3E%3Cg%20class%3D%22g-ytitle%22%3E%3Ctext%20class%3D%22ytitle%22%20transform%3D%22rotate(-90%2C42%2C104.8421052631579)%22%20x%3D%2242%22%20y%3D%22104.8421052631579%22%20text-anchor%3D%22middle%22%20data-unformatted%3D%22Red%22%20data-math%3D%22N%22%20style%3D%22font-family%3A%20%26quot%3BOpen%20Sans%26quot%3B%2C%20verdana%2C%20arial%2C%20sans-serif%3B%20font-size%3A%2014px%3B%20fill%3A%20rgb(89%2C%2089%2C%2089)%3B%20opacity%3A%201%3B%20font-weight%3A%20normal%3B%20white-space%3A%20pre%3B%22%3ERed%3C%2Ftext%3E%3C%2Fg%3E%3Cg%20class%3D%22g-y2title%22%3E%3Ctext%20class%3D%22y2title%22%20transform%3D%22rotate(-90%2C42%2C271.15789473684214)%22%20x%3D%2242%22%20y%3D%22271.15789473684214%22%20text-anchor%3D%22middle%22%20data-unformatted%3D%22White%22%20data-math%3D%22N%22%20style%3D%22font-family%3A%20%26quot%3BOpen%20Sans%26quot%3B%2C%20verdana%2C%20arial%2C%20sans-serif%3B%20font-size%3A%2014px%3B%20fill%3A%20rgb(89%2C%2089%2C%2089)%3B%20opacity%3A%201%3B%20font-weight%3A%20normal%3B%20white-space%3A%20pre%3B%22%3EWhite%3C%2Ftext%3E%3C%2Fg%3E%3C%2Fg%3E%3Cg%20class%3D%22menulayer%22%2F%3E%3Cg%20class%3D%22zoomlayer%22%2F%3E%3Cg%20class%3D%22hoverlayer%22%2F%3E%3C%2Fsvg%3E\">"
      ],
      "text/plain": [
       "Open the notebook in JupyterLab with the atoti extension enabled to build this widget."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "session.visualize(\"Wine characteristics comparison\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0928b2a-58ea-448e-8f2f-f53b2394f272",
   "metadata": {},
   "source": [
    "### Creating hierarchy from numerical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cced15c4-23c1-4140-a749-37e1bccd55c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "h, m = cube.hierarchies, cube.measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ec5b12f4-5087-452f-b92b-78d46cd3f744",
   "metadata": {},
   "outputs": [],
   "source": [
    "h[\"value\"] = [wines_table[\"value\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "00ef432d-6419-4fa6-9eb2-0b678434aa4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/json": {
       "Wine detail": {
        "category": [
         "category"
        ],
        "rating": [
         "rating"
        ]
       },
       "Wine quality": {
        "value": [
         "value"
        ],
        "variable": [
         "variable"
        ],
        "wine index": [
         "wine index"
        ]
       }
      },
      "text/html": [
       "<ul>\n",
       "<li>Dimensions\n",
       "  <ul>\n",
       "  <li>Wine detail\n",
       "    <ul>\n",
       "    <li>category\n",
       "      <ol>\n",
       "      <li>category</li>\n",
       "      </ol>    </li>\n",
       "    <li>rating\n",
       "      <ol>\n",
       "      <li>rating</li>\n",
       "      </ol>    </li>\n",
       "    </ul>\n",
       "  </li>\n",
       "  <li>Wine quality\n",
       "    <ul>\n",
       "    <li>value\n",
       "      <ol>\n",
       "      <li>value</li>\n",
       "      </ol>    </li>\n",
       "    <li>variable\n",
       "      <ol>\n",
       "      <li>variable</li>\n",
       "      </ol>    </li>\n",
       "    <li>wine index\n",
       "      <ol>\n",
       "      <li>wine index</li>\n",
       "      </ol>    </li>\n",
       "    </ul>\n",
       "  </li>\n",
       "  </ul>\n",
       "</li>\n",
       "</ul>\n"
      ],
      "text/plain": [
       "Hierarchies()"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "application/json": {
       "expanded": true,
       "root": "Dimensions"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ee654aa-1ad7-43b6-bce3-a79a9b73873a",
   "metadata": {},
   "source": [
    "### Creating simple measure from referenced table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a8fdcf57-18b6-48fd-9cf3-8e6b70c4e3da",
   "metadata": {},
   "outputs": [],
   "source": [
    "m[\"quality\"] = tt.agg.mean(wines_detail[\"quality\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8ff505ca-3ef7-45aa-ba61-f5dcbd735199",
   "metadata": {
    "atoti": {
     "widget": {
      "filters": [
       "[Wine quality].[variable].[AllMember].[volatile acidity]"
      ],
      "mapping": {
       "horizontalSubplots": [],
       "splitBy": [
        "[Wine detail].[category].[category]",
        "[Wine quality].[variable].[variable]",
        "ALL_MEASURES"
       ],
       "values": [
        "[Measures].[quality]"
       ],
       "verticalSubplots": [],
       "xAxis": [
        "[Wine quality].[value].[value]"
       ]
      },
      "query": {
       "mdx": "SELECT NON EMPTY Crossjoin(Hierarchize(Descendants({[Wine detail].[category].[AllMember]}, 1, SELF_AND_BEFORE)), Hierarchize(Descendants({[Wine quality].[variable].[AllMember]}, 1, SELF_AND_BEFORE)), {[Measures].[quality]}) ON COLUMNS, NON EMPTY Hierarchize(Descendants({[Wine quality].[value].[AllMember]}, 1, SELF_AND_BEFORE)) ON ROWS FROM [Wines] CELL PROPERTIES VALUE, FORMATTED_VALUE, BACK_COLOR, FORE_COLOR, FONT_FLAGS",
       "updateMode": "once"
      },
      "serverKey": "default",
      "widgetKey": "plotly-line-chart"
     }
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.atoti.widget.v0+json": {
       "name": "Features vs Wine quality",
       "sessionId": "8a9b87cb-6269-4eaa-8611-7f16c6585f5e",
       "sessionLocation": {
        "https": false,
        "port": 63906
       },
       "widgetCreationCode": "session.visualize()"
      },
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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": [
    "session.visualize(\"Features vs Wine quality\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c118b7b1-46c3-456c-a816-4b84289c2cee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.atoti.link.v0+json": {
       "path": "#/dashboard/29f",
       "sessionLocation": {
        "https": false,
        "port": 63906
       }
      },
      "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/29f\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6438c75a-6054-484d-9d76-c9db133358f9",
   "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}