assets/notebooks/sklearn-pandas-df-output.ipynb

{
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   "cell_type": "code",
   "execution_count": 1,
   "id": "10353174-f58e-4885-aba2-00c9b5b0ab24",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(\"ignore\", FutureWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e885c43c",
   "metadata": {},
   "source": [
    "# scikit-learn\n",
    "## Example: Pandas DataFrame output for sklearn transformer\n",
    "\n",
    "### Outline\n",
    "- Example 1a: iris dataset (`StandardScalar` transformation)\n",
    "- Example 1b: iris dataset (`PolynomialFeatures` transformation)\n",
    "- Example 2: titanic dataset (with a Pipeline)\n",
    "\n",
    "### Prepared by\n",
    "- Andreas Mueller\n",
    "- Reshama Shaikh\n",
    "\n",
    "### Date\n",
    "November 2022"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a711bdd1",
   "metadata": {},
   "source": [
    "# Example 1a: iris dataset (`StandardScalar` transformation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c327cbbd-44c6-4795-80fb-109c11707e77",
   "metadata": {},
   "outputs": [
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       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
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       "      <th>60</th>\n",
       "      <td>5.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.7</td>\n",
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       "      <th>66</th>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
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       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
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       "      <th>111</th>\n",
       "      <td>6.4</td>\n",
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       "      <td>5.3</td>\n",
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       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "60                 5.0               2.0                3.5               1.0\n",
       "1                  4.9               3.0                1.4               0.2\n",
       "8                  4.4               2.9                1.4               0.2\n",
       "93                 5.0               2.3                3.3               1.0\n",
       "106                4.9               2.5                4.5               1.7\n",
       "..                 ...               ...                ...               ...\n",
       "66                 5.6               3.0                4.5               1.5\n",
       "29                 4.7               3.2                1.6               0.2\n",
       "130                7.4               2.8                6.1               1.9\n",
       "141                6.9               3.1                5.1               2.3\n",
       "111                6.4               2.7                5.3               1.9\n",
       "\n",
       "[112 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X, y = load_iris(as_frame=True, return_X_y=True)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0)\n",
    "X_train"
   ]
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       "       [-1.47793352,  0.7983005 , -1.32740725, -1.19613545],\n",
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       "       [ 1.55714575,  1.24092256,  1.30440886,  1.69618815]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n",
    "\n",
    "# set the transform method\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# transform the training data\n",
    "scaler.fit(X_train)\n",
    "\n",
    "# transform the test data\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Let's look at the output.\n",
    "# Input data:  X_train was a pandas dataframe\n",
    "# Output data: X_test_scaled is a numpy array\n",
    "\n",
    "X_test_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01a80bd5",
   "metadata": {},
   "source": [
    "# Example 1b: iris dataset (`PolynomialFeatures` transformation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "72650a0b-3ad1-44d8-9a95-6441903cd71b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.  ,  5.  ,  2.  , ..., 12.25,  3.5 ,  1.  ],\n",
       "       [ 1.  ,  4.9 ,  3.  , ...,  1.96,  0.28,  0.04],\n",
       "       [ 1.  ,  4.4 ,  2.9 , ...,  1.96,  0.28,  0.04],\n",
       "       ...,\n",
       "       [ 1.  ,  7.4 ,  2.8 , ..., 37.21, 11.59,  3.61],\n",
       "       [ 1.  ,  6.9 ,  3.1 , ..., 26.01, 11.73,  5.29],\n",
       "       [ 1.  ,  6.4 ,  2.7 , ..., 28.09, 10.07,  3.61]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We transform the training data (X_train) to polynomial features\n",
    "# Output: type is ...\n",
    "PolynomialFeatures().fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c649ffc",
   "metadata": {},
   "source": [
    "### Use `transform_output`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bd241d45-f95f-49d5-8b66-17adf66160d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import set_config\n",
    "set_config(transform_output=\"pandas\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fce8d4ee-fd9d-4ed0-a5d3-e6c6e81bb1e4",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>sepal length (cm)^2</th>\n",
       "      <th>sepal length (cm) sepal width (cm)</th>\n",
       "      <th>sepal length (cm) petal length (cm)</th>\n",
       "      <th>sepal length (cm) petal width (cm)</th>\n",
       "      <th>sepal width (cm)^2</th>\n",
       "      <th>sepal width (cm) petal length (cm)</th>\n",
       "      <th>sepal width (cm) petal width (cm)</th>\n",
       "      <th>petal length (cm)^2</th>\n",
       "      <th>petal length (cm) petal width (cm)</th>\n",
       "      <th>petal width (cm)^2</th>\n",
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       "      <th>60</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>24.01</td>\n",
       "      <td>14.70</td>\n",
       "      <td>6.86</td>\n",
       "      <td>0.98</td>\n",
       "      <td>9.00</td>\n",
       "      <td>4.20</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.96</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.04</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>19.36</td>\n",
       "      <td>12.76</td>\n",
       "      <td>6.16</td>\n",
       "      <td>0.88</td>\n",
       "      <td>8.41</td>\n",
       "      <td>4.06</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.96</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.04</td>\n",
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       "      <th>93</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>11.50</td>\n",
       "      <td>16.50</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.29</td>\n",
       "      <td>7.59</td>\n",
       "      <td>2.30</td>\n",
       "      <td>10.89</td>\n",
       "      <td>3.30</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>24.01</td>\n",
       "      <td>12.25</td>\n",
       "      <td>22.05</td>\n",
       "      <td>8.33</td>\n",
       "      <td>6.25</td>\n",
       "      <td>11.25</td>\n",
       "      <td>4.25</td>\n",
       "      <td>20.25</td>\n",
       "      <td>7.65</td>\n",
       "      <td>2.89</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <th>66</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>31.36</td>\n",
       "      <td>16.80</td>\n",
       "      <td>25.20</td>\n",
       "      <td>8.40</td>\n",
       "      <td>9.00</td>\n",
       "      <td>13.50</td>\n",
       "      <td>4.50</td>\n",
       "      <td>20.25</td>\n",
       "      <td>6.75</td>\n",
       "      <td>2.25</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>22.09</td>\n",
       "      <td>15.04</td>\n",
       "      <td>7.52</td>\n",
       "      <td>0.94</td>\n",
       "      <td>10.24</td>\n",
       "      <td>5.12</td>\n",
       "      <td>0.64</td>\n",
       "      <td>2.56</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>54.76</td>\n",
       "      <td>20.72</td>\n",
       "      <td>45.14</td>\n",
       "      <td>14.06</td>\n",
       "      <td>7.84</td>\n",
       "      <td>17.08</td>\n",
       "      <td>5.32</td>\n",
       "      <td>37.21</td>\n",
       "      <td>11.59</td>\n",
       "      <td>3.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>47.61</td>\n",
       "      <td>21.39</td>\n",
       "      <td>35.19</td>\n",
       "      <td>15.87</td>\n",
       "      <td>9.61</td>\n",
       "      <td>15.81</td>\n",
       "      <td>7.13</td>\n",
       "      <td>26.01</td>\n",
       "      <td>11.73</td>\n",
       "      <td>5.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>40.96</td>\n",
       "      <td>17.28</td>\n",
       "      <td>33.92</td>\n",
       "      <td>12.16</td>\n",
       "      <td>7.29</td>\n",
       "      <td>14.31</td>\n",
       "      <td>5.13</td>\n",
       "      <td>28.09</td>\n",
       "      <td>10.07</td>\n",
       "      <td>3.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       1  sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
       "60   1.0                5.0               2.0                3.5   \n",
       "1    1.0                4.9               3.0                1.4   \n",
       "8    1.0                4.4               2.9                1.4   \n",
       "93   1.0                5.0               2.3                3.3   \n",
       "106  1.0                4.9               2.5                4.5   \n",
       "..   ...                ...               ...                ...   \n",
       "66   1.0                5.6               3.0                4.5   \n",
       "29   1.0                4.7               3.2                1.6   \n",
       "130  1.0                7.4               2.8                6.1   \n",
       "141  1.0                6.9               3.1                5.1   \n",
       "111  1.0                6.4               2.7                5.3   \n",
       "\n",
       "     petal width (cm)  sepal length (cm)^2  \\\n",
       "60                1.0                25.00   \n",
       "1                 0.2                24.01   \n",
       "8                 0.2                19.36   \n",
       "93                1.0                25.00   \n",
       "106               1.7                24.01   \n",
       "..                ...                  ...   \n",
       "66                1.5                31.36   \n",
       "29                0.2                22.09   \n",
       "130               1.9                54.76   \n",
       "141               2.3                47.61   \n",
       "111               1.9                40.96   \n",
       "\n",
       "     sepal length (cm) sepal width (cm)  sepal length (cm) petal length (cm)  \\\n",
       "60                                10.00                                17.50   \n",
       "1                                 14.70                                 6.86   \n",
       "8                                 12.76                                 6.16   \n",
       "93                                11.50                                16.50   \n",
       "106                               12.25                                22.05   \n",
       "..                                  ...                                  ...   \n",
       "66                                16.80                                25.20   \n",
       "29                                15.04                                 7.52   \n",
       "130                               20.72                                45.14   \n",
       "141                               21.39                                35.19   \n",
       "111                               17.28                                33.92   \n",
       "\n",
       "     sepal length (cm) petal width (cm)  sepal width (cm)^2  \\\n",
       "60                                 5.00                4.00   \n",
       "1                                  0.98                9.00   \n",
       "8                                  0.88                8.41   \n",
       "93                                 5.00                5.29   \n",
       "106                                8.33                6.25   \n",
       "..                                  ...                 ...   \n",
       "66                                 8.40                9.00   \n",
       "29                                 0.94               10.24   \n",
       "130                               14.06                7.84   \n",
       "141                               15.87                9.61   \n",
       "111                               12.16                7.29   \n",
       "\n",
       "     sepal width (cm) petal length (cm)  sepal width (cm) petal width (cm)  \\\n",
       "60                                 7.00                               2.00   \n",
       "1                                  4.20                               0.60   \n",
       "8                                  4.06                               0.58   \n",
       "93                                 7.59                               2.30   \n",
       "106                               11.25                               4.25   \n",
       "..                                  ...                                ...   \n",
       "66                                13.50                               4.50   \n",
       "29                                 5.12                               0.64   \n",
       "130                               17.08                               5.32   \n",
       "141                               15.81                               7.13   \n",
       "111                               14.31                               5.13   \n",
       "\n",
       "     petal length (cm)^2  petal length (cm) petal width (cm)  \\\n",
       "60                 12.25                                3.50   \n",
       "1                   1.96                                0.28   \n",
       "8                   1.96                                0.28   \n",
       "93                 10.89                                3.30   \n",
       "106                20.25                                7.65   \n",
       "..                   ...                                 ...   \n",
       "66                 20.25                                6.75   \n",
       "29                  2.56                                0.32   \n",
       "130                37.21                               11.59   \n",
       "141                26.01                               11.73   \n",
       "111                28.09                               10.07   \n",
       "\n",
       "     petal width (cm)^2  \n",
       "60                 1.00  \n",
       "1                  0.04  \n",
       "8                  0.04  \n",
       "93                 1.00  \n",
       "106                2.89  \n",
       "..                  ...  \n",
       "66                 2.25  \n",
       "29                 0.04  \n",
       "130                3.61  \n",
       "141                5.29  \n",
       "111                3.61  \n",
       "\n",
       "[112 rows x 15 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output: type is ...\n",
    "PolynomialFeatures().fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "644cc7e7",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a035d647",
   "metadata": {},
   "source": [
    "# Example 2: titanic dataset (with a Pipeline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2704fbbb-eefd-4fdb-a7ae-428eef539d54",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's go back to the default settings\n",
    "set_config(transform_output=\"default\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dff9ffc1-daa0-4de1-b494-a84a3b12816e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X, y = fetch_openml(\"titanic\", version=1, as_frame=True, return_X_y=True)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2ba9f7e6-d903-42d0-a56e-4c514c83a2c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>pclass</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Holm, Mr. John Fredrik Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>43.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C 7075</td>\n",
       "      <td>6.4500</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Lobb, Mr. William Arthur</td>\n",
       "      <td>male</td>\n",
       "      <td>30.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A/5. 3336</td>\n",
       "      <td>16.1000</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Cacic, Mr. Luka</td>\n",
       "      <td>male</td>\n",
       "      <td>38.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>315089</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Croatia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1044</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Murphy, Miss. Nora</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36568</td>\n",
       "      <td>15.5000</td>\n",
       "      <td>None</td>\n",
       "      <td>Q</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Renouf, Mrs. Peter Henry (Lillian Jefferys)</td>\n",
       "      <td>female</td>\n",
       "      <td>30.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31027</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Elizabeth, NJ</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Peacock, Master. Alfred Edward</td>\n",
       "      <td>male</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>SOTON/O.Q. 3101315</td>\n",
       "      <td>13.7750</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1123</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Peter, Mrs. Catherine (Catherine Rizk)</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>None</td>\n",
       "      <td>C</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Fortune, Miss. Ethel Flora</td>\n",
       "      <td>female</td>\n",
       "      <td>28.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Winnipeg, MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Yousseff, Mr. Gerious</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2627</td>\n",
       "      <td>14.4583</td>\n",
       "      <td>None</td>\n",
       "      <td>C</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Cumings, Mr. John Bradley</td>\n",
       "      <td>male</td>\n",
       "      <td>39.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York, NY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>981 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      pclass                                         name     sex    age  \\\n",
       "868      3.0             Holm, Mr. John Fredrik Alexander    male  43.00   \n",
       "974      3.0                     Lobb, Mr. William Arthur    male  30.00   \n",
       "699      3.0                              Cacic, Mr. Luka    male  38.00   \n",
       "1044     3.0                           Murphy, Miss. Nora  female    NaN   \n",
       "545      2.0  Renouf, Mrs. Peter Henry (Lillian Jefferys)  female  30.00   \n",
       "...      ...                                          ...     ...    ...   \n",
       "1111     3.0               Peacock, Master. Alfred Edward    male   0.75   \n",
       "1123     3.0       Peter, Mrs. Catherine (Catherine Rizk)  female    NaN   \n",
       "112      1.0                   Fortune, Miss. Ethel Flora  female  28.00   \n",
       "1303     3.0                        Yousseff, Mr. Gerious    male    NaN   \n",
       "84       1.0                    Cumings, Mr. John Bradley    male  39.00   \n",
       "\n",
       "      sibsp  parch              ticket      fare        cabin embarked  boat  \\\n",
       "868     0.0    0.0              C 7075    6.4500         None        S  None   \n",
       "974     1.0    0.0           A/5. 3336   16.1000         None        S  None   \n",
       "699     0.0    0.0              315089    8.6625         None        S  None   \n",
       "1044    0.0    0.0               36568   15.5000         None        Q    16   \n",
       "545     3.0    0.0               31027   21.0000         None        S  None   \n",
       "...     ...    ...                 ...       ...          ...      ...   ...   \n",
       "1111    1.0    1.0  SOTON/O.Q. 3101315   13.7750         None        S  None   \n",
       "1123    0.0    2.0                2668   22.3583         None        C     D   \n",
       "112     3.0    2.0               19950  263.0000  C23 C25 C27        S    10   \n",
       "1303    0.0    0.0                2627   14.4583         None        C  None   \n",
       "84      1.0    0.0            PC 17599   71.2833          C85        C  None   \n",
       "\n",
       "     body      home.dest  \n",
       "868   NaN           None  \n",
       "974   NaN           None  \n",
       "699   NaN        Croatia  \n",
       "1044  NaN           None  \n",
       "545   NaN  Elizabeth, NJ  \n",
       "...   ...            ...  \n",
       "1111  NaN           None  \n",
       "1123  NaN           None  \n",
       "112   NaN   Winnipeg, MB  \n",
       "1303  NaN           None  \n",
       "84    NaN   New York, NY  \n",
       "\n",
       "[981 rows x 13 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "736d3546-051b-4eca-bdee-3aa885c0302b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;pipeline&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;standardscaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;age&#x27;, &#x27;fare&#x27;]),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(sparse=False),\n",
       "                                                  [&#x27;embarked&#x27;, &#x27;sex&#x27;,\n",
       "                                                   &#x27;pclass&#x27;])],\n",
       "                                   verbose_feature_names_out=False)),\n",
       "                (&#x27;logisticregression&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;pipeline&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;standardscaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;age&#x27;, &#x27;fare&#x27;]),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(sparse=False),\n",
       "                                                  [&#x27;embarked&#x27;, &#x27;sex&#x27;,\n",
       "                                                   &#x27;pclass&#x27;])],\n",
       "                                   verbose_feature_names_out=False)),\n",
       "                (&#x27;logisticregression&#x27;, LogisticRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;pipeline&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer()),\n",
       "                                                 (&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler())]),\n",
       "                                 [&#x27;age&#x27;, &#x27;fare&#x27;]),\n",
       "                                (&#x27;onehotencoder&#x27;, OneHotEncoder(sparse=False),\n",
       "                                 [&#x27;embarked&#x27;, &#x27;sex&#x27;, &#x27;pclass&#x27;])],\n",
       "                  verbose_feature_names_out=False)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">pipeline</label><div class=\"sk-toggleable__content\"><pre>[&#x27;age&#x27;, &#x27;fare&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">onehotencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;embarked&#x27;, &#x27;sex&#x27;, &#x27;pclass&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(sparse=False)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('columntransformer',\n",
       "                 ColumnTransformer(transformers=[('pipeline',\n",
       "                                                  Pipeline(steps=[('simpleimputer',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('standardscaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['age', 'fare']),\n",
       "                                                 ('onehotencoder',\n",
       "                                                  OneHotEncoder(sparse=False),\n",
       "                                                  ['embarked', 'sex',\n",
       "                                                   'pclass'])],\n",
       "                                   verbose_feature_names_out=False)),\n",
       "                ('logisticregression', LogisticRegression())])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Here we use `StandardScaler` for continuous variables; \n",
    "#      then we impute for missing data (check the documentation for the imputation method)\n",
    "# We use `OneHotEncoder` for categorical variables\n",
    "# NOTE: we are using a subset of the features (not all the columns)\n",
    "\n",
    "ct = make_column_transformer((make_pipeline(SimpleImputer(), \n",
    "                                            StandardScaler()), [\"age\", \"fare\"]),\n",
    "                             (OneHotEncoder(sparse=False), [\"embarked\", \"sex\", \"pclass\"]), \n",
    "                             verbose_feature_names_out=False)\n",
    "\n",
    "# Note: click on pipeline elements to see more details\n",
    "clf = make_pipeline(ct, LogisticRegression())\n",
    "clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6a88f293-9019-4915-b526-f7a1f871c518",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7865853658536586"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)\n",
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "549b42bd-c84a-4348-9ecb-b261909dca9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.44376706, -0.50468405,  0.        , ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       [-0.44376706,  0.05957877,  1.        , ...,  0.        ,\n",
       "         1.        ,  0.        ],\n",
       "       [ 0.47033007, -0.34728169,  0.        , ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       ...,\n",
       "       [ 0.39415531,  0.45300503,  1.        , ...,  1.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.01328151, -0.14910643,  0.        , ...,  1.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-0.82464087, -0.39564025,  0.        , ...,  0.        ,\n",
       "         1.        ,  0.        ]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's remove the last step in the pipeline (which is LogisticRegression()) & transform the X_test data\n",
    "\n",
    "clf[:-1].transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "13d10de4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7028ada4",
   "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>pclass</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Colbert, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>371109</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>None</td>\n",
       "      <td>Q</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Co Limerick, Ireland Sherbrooke, PQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Mallet, Mrs. Albert (Antoinette Magnin)</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>S.C./PARIS 2079</td>\n",
       "      <td>37.0042</td>\n",
       "      <td>None</td>\n",
       "      <td>C</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris / Montreal, PQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>968</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Lindell, Mr. Edvard Bengtsson</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>349910</td>\n",
       "      <td>15.5500</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     pclass                                     name     sex   age  sibsp  \\\n",
       "720     3.0                     Colbert, Mr. Patrick    male  24.0    0.0   \n",
       "494     2.0  Mallet, Mrs. Albert (Antoinette Magnin)  female  24.0    1.0   \n",
       "968     3.0            Lindell, Mr. Edvard Bengtsson    male  36.0    1.0   \n",
       "\n",
       "     parch           ticket     fare cabin embarked  boat body  \\\n",
       "720    0.0           371109   7.2500  None        Q  None  NaN   \n",
       "494    1.0  S.C./PARIS 2079  37.0042  None        C    10  NaN   \n",
       "968    0.0           349910  15.5500  None        S     A  NaN   \n",
       "\n",
       "                               home.dest  \n",
       "720  Co Limerick, Ireland Sherbrooke, PQ  \n",
       "494                 Paris / Montreal, PQ  \n",
       "968                                 None  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# untransformed data (has all the orginal columns)\n",
    "X_test.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "664872d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S      222\n",
       "C       68\n",
       "Q       37\n",
       "NaN      1\n",
       "Name: embarked, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test['embarked'].value_counts(normalize=False, dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c7bdf8bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      211\n",
       "female    117\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test['sex'].value_counts(normalize=False, dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d672e590",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    184\n",
       "1.0     80\n",
       "2.0     64\n",
       "Name: pclass, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test['pclass'].value_counts(normalize=False, dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6437e2f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Notes\n",
    "# a) Notice first two columns are numbers and from our transformer should be \"age\" and \"fare\"\n",
    "# b) Next 4 cols are embarked (4 possible values); sex (2 possible values here); pclass (3 possible values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ed70d7ba-8b82-4cb8-a0ed-ae3825222134",
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>8</th>\n",
       "      <th>9</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.443767</td>\n",
       "      <td>-0.504684</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.470330</td>\n",
       "      <td>-0.347282</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.146175</td>\n",
       "      <td>2.268110</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.536777</td>\n",
       "      <td>1.376085</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>-1.662563</td>\n",
       "      <td>0.054758</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>2.450874</td>\n",
       "      <td>0.874957</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>0.394155</td>\n",
       "      <td>0.453005</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>0.013282</td>\n",
       "      <td>-0.149106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>-0.824641</td>\n",
       "      <td>-0.395640</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>328 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1    2    3    4    5    6    7    8    9    10\n",
       "0   -0.443767 -0.504684  0.0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  1.0\n",
       "1   -0.443767  0.059579  1.0  0.0  0.0  0.0  1.0  0.0  0.0  1.0  0.0\n",
       "2    0.470330 -0.347282  0.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  1.0\n",
       "3    2.146175  2.268110  0.0  0.0  1.0  0.0  1.0  0.0  1.0  0.0  0.0\n",
       "4    1.536777  1.376085  1.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0\n",
       "..        ...       ...  ...  ...  ...  ...  ...  ...  ...  ...  ...\n",
       "323 -1.662563  0.054758  0.0  0.0  1.0  0.0  0.0  1.0  0.0  1.0  0.0\n",
       "324  2.450874  0.874957  0.0  0.0  0.0  1.0  1.0  0.0  1.0  0.0  0.0\n",
       "325  0.394155  0.453005  1.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0  0.0\n",
       "326  0.013282 -0.149106  0.0  0.0  1.0  0.0  0.0  1.0  1.0  0.0  0.0\n",
       "327 -0.824641 -0.395640  0.0  0.0  1.0  0.0  0.0  1.0  0.0  1.0  0.0\n",
       "\n",
       "[328 rows x 11 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transformed data\n",
    "pd.DataFrame(clf[:-1].transform(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4d5d154a-8412-44a8-ac16-b7e7212bfd92",
   "metadata": {},
   "outputs": [],
   "source": [
    "set_config(transform_output=\"pandas\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6cdd01e4-aef1-4ee1-97bf-f3e5b866c725",
   "metadata": {},
   "outputs": [
    {
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       "      <th>385</th>\n",
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       "<p>328 rows × 11 columns</p>\n",
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      "text/plain": [
       "          age      fare  embarked_C  embarked_Q  embarked_S  embarked_nan  \\\n",
       "720 -0.443767 -0.504684         0.0         1.0         0.0           0.0   \n",
       "494 -0.443767  0.059579         1.0         0.0         0.0           0.0   \n",
       "968  0.470330 -0.347282         0.0         0.0         1.0           0.0   \n",
       "139  2.146175  2.268110         0.0         0.0         1.0           0.0   \n",
       "96   1.536777  1.376085         1.0         0.0         0.0           0.0   \n",
       "..        ...       ...         ...         ...         ...           ...   \n",
       "385 -1.662563  0.054758         0.0         0.0         1.0           0.0   \n",
       "284  2.450874  0.874957         0.0         0.0         0.0           1.0   \n",
       "257  0.394155  0.453005         1.0         0.0         0.0           0.0   \n",
       "194  0.013282 -0.149106         0.0         0.0         1.0           0.0   \n",
       "364 -0.824641 -0.395640         0.0         0.0         1.0           0.0   \n",
       "\n",
       "     sex_female  sex_male  pclass_1.0  pclass_2.0  pclass_3.0  \n",
       "720         0.0       1.0         0.0         0.0         1.0  \n",
       "494         1.0       0.0         0.0         1.0         0.0  \n",
       "968         0.0       1.0         0.0         0.0         1.0  \n",
       "139         1.0       0.0         1.0         0.0         0.0  \n",
       "96          0.0       1.0         1.0         0.0         0.0  \n",
       "..          ...       ...         ...         ...         ...  \n",
       "385         0.0       1.0         0.0         1.0         0.0  \n",
       "284         1.0       0.0         1.0         0.0         0.0  \n",
       "257         1.0       0.0         1.0         0.0         0.0  \n",
       "194         0.0       1.0         1.0         0.0         0.0  \n",
       "364         0.0       1.0         0.0         1.0         0.0  \n",
       "\n",
       "[328 rows x 11 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)\n",
    "\n",
    "X_test_transformed = clf[:-1].transform(X_test)\n",
    "X_test_transformed "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6886048a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.3.3\n",
      "1.21.2\n",
      "1.2.dev0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import sklearn\n",
    "print(pd.__version__)\n",
    "print(np.__version__)\n",
    "print(sklearn.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8e8c2b2",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf92c4e3",
   "metadata": {},
   "source": [
    "# Resources\n",
    "\n",
    "## Documentation\n",
    "- Documentation of `set_output` API:  https://scikit-learn.org/dev/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py\n",
    "\n",
    "## Try out the dev version\n",
    "- scikit-learn 1.2.dev0\n",
    "- Installing the nightly build:\n",
    "http://scikit-learn.org/stable/developers/advanced_installation.html\n",
    "\n",
    "## Report any bugs or issues\n",
    "#### We'd love to hear both about whether this helps your use cases and any bugs you find!\n",
    "#### We are also specifically looking for feedback from library authors on how their experience is and if this change introduces any unexpected hassles.\n",
    "Post any issues or bugs here:  https://github.com/scikit-learn/scikit-learn/issues"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0ee7985",
   "metadata": {},
   "source": [
    "# FAQs\n",
    "\n",
    "### Q1: Why did this update take so long?\n",
    "> There was no established dataframe when scikit-learn was initially released - and it was released in the context of scientific computing, in which dataframes make less sense. Adding it back in later was quite tricky because of the interactions of numpy and pandas, and the lack of annotated sparse formats.\n",
    "\n",
    "### Q2: Will pandas output be supported also in other estimators?\n",
    "> This is still work in progress! Let us know what your use-cases are! What would you like to see?\n",
    "\n",
    "### Q3: Does this mean we could chain Column Transformers while referencing column names?\n",
    "> Yes\n",
    "\n",
    "### Q4: When will version 1.2 be released?\n",
    "> End of 2022!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e21eaaa5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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