diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 111b8712..863c0594 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -7,16 +7,14 @@ repos:
       - id: trailing-whitespace
       - id: mixed-line-ending
 
-  - repo: local
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    # Ruff version.
+    rev: v0.8.0
     hooks:
-      - id: black
-        name: black
-        language: python
-        types: [python]
-        entry: black
-
+      # Run the linter.
       - id: ruff
-        name: ruff
-        language: python
-        types: [python]
-        entry: ruff
+        types_or: [python, pyi]
+        args: [--fix]
+      # Run the formatter.
+      - id: ruff-format
+        types_or: [python, pyi]
diff --git a/docs/content/famd.ipynb b/docs/content/famd.ipynb
index b6c02050..dadfe379 100644
--- a/docs/content/famd.ipynb
+++ b/docs/content/famd.ipynb
@@ -21,7 +21,7 @@
    "source": [
     "## Resources\n",
     "\n",
-    "🤷‍♂️"
+    "- [Wikipedia article](https://en.wikipedia.org/wiki/Factor_analysis_of_mixed_data)"
    ]
   },
   {
diff --git a/docs/content/mca.ipynb b/docs/content/mca.ipynb
index 4bb49112..cfc5a088 100644
--- a/docs/content/mca.ipynb
+++ b/docs/content/mca.ipynb
@@ -938,7 +938,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "prince-NQ1O93Uh-py3.11",
    "language": "python",
    "name": "python3"
   },
@@ -953,11 +953,6 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.11.4"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "441c2ec70d9faeb70e7723f55150c6260f4a26a9c828b90915d3399002e14f43"
-   }
   }
  },
  "nbformat": 4,
diff --git a/docs/content/mfa.ipynb b/docs/content/mfa.ipynb
index d75d5e57..30fab5a5 100644
--- a/docs/content/mfa.ipynb
+++ b/docs/content/mfa.ipynb
@@ -27,7 +27,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "- [Multiple Factor Analysis](https://www.utdallas.edu/~herve/Abdi-MFA2007-pretty.pdf)"
+    "- [*Multiple Factor Analysis* by Hervé Abdi](https://www.utdallas.edu/~herve/Abdi-MFA2007-pretty.pdf)\n",
+    "- [*Multiple Factor Analysis: main features and application to sensory data* by Jérôme Pagès](http://factominer.free.fr/more/PagesAFM.pdf)\n",
+    "- [Wikipedia article](https://en.wikipedia.org/wiki/Multiple_factor_analysis)"
    ]
   },
   {
@@ -70,138 +72,397 @@
        "    .dataframe thead tr th {\n",
        "        text-align: left;\n",
        "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
-       "      <th>expert</th>\n",
-       "      <th>Oak type</th>\n",
-       "      <th colspan=\"3\" halign=\"left\">Expert 1</th>\n",
-       "      <th colspan=\"4\" halign=\"left\">Expert 2</th>\n",
-       "      <th colspan=\"3\" halign=\"left\">Expert 3</th>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"6\" halign=\"left\">2021-22</th>\n",
+       "      <th colspan=\"6\" halign=\"left\">2022-23</th>\n",
+       "      <th colspan=\"6\" halign=\"left\">2023-24</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>aspect</th>\n",
        "      <th></th>\n",
-       "      <th>Fruity</th>\n",
-       "      <th>Woody</th>\n",
-       "      <th>Coffee</th>\n",
-       "      <th>Red fruit</th>\n",
-       "      <th>Roasted</th>\n",
-       "      <th>Vanillin</th>\n",
-       "      <th>Woody</th>\n",
-       "      <th>Fruity</th>\n",
-       "      <th>Butter</th>\n",
-       "      <th>Woody</th>\n",
+       "      <th>W</th>\n",
+       "      <th>D</th>\n",
+       "      <th>L</th>\n",
+       "      <th>GF</th>\n",
+       "      <th>GA</th>\n",
+       "      <th>Pts</th>\n",
+       "      <th>W</th>\n",
+       "      <th>D</th>\n",
+       "      <th>L</th>\n",
+       "      <th>GF</th>\n",
+       "      <th>GA</th>\n",
+       "      <th>Pts</th>\n",
+       "      <th>W</th>\n",
+       "      <th>D</th>\n",
+       "      <th>L</th>\n",
+       "      <th>GF</th>\n",
+       "      <th>GA</th>\n",
+       "      <th>Pts</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Team</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",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>Wine 1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
+       "      <th>Arsenal</th>\n",
+       "      <td>22</td>\n",
+       "      <td>3</td>\n",
+       "      <td>13</td>\n",
+       "      <td>61</td>\n",
+       "      <td>48</td>\n",
+       "      <td>69</td>\n",
+       "      <td>26</td>\n",
        "      <td>6</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>88</td>\n",
+       "      <td>43</td>\n",
+       "      <td>84</td>\n",
+       "      <td>28</td>\n",
+       "      <td>5</td>\n",
        "      <td>5</td>\n",
+       "      <td>91</td>\n",
+       "      <td>29</td>\n",
+       "      <td>89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Aston Villa</th>\n",
+       "      <td>13</td>\n",
+       "      <td>6</td>\n",
+       "      <td>19</td>\n",
+       "      <td>52</td>\n",
+       "      <td>54</td>\n",
+       "      <td>45</td>\n",
+       "      <td>18</td>\n",
        "      <td>7</td>\n",
+       "      <td>13</td>\n",
+       "      <td>51</td>\n",
+       "      <td>46</td>\n",
+       "      <td>61</td>\n",
+       "      <td>20</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>76</td>\n",
+       "      <td>61</td>\n",
+       "      <td>68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Brentford</th>\n",
+       "      <td>13</td>\n",
+       "      <td>7</td>\n",
+       "      <td>18</td>\n",
+       "      <td>48</td>\n",
+       "      <td>56</td>\n",
+       "      <td>46</td>\n",
+       "      <td>15</td>\n",
+       "      <td>14</td>\n",
+       "      <td>9</td>\n",
+       "      <td>58</td>\n",
+       "      <td>46</td>\n",
+       "      <td>59</td>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19</td>\n",
+       "      <td>56</td>\n",
+       "      <td>65</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Brighton &amp; Hove Albion</th>\n",
+       "      <td>12</td>\n",
+       "      <td>15</td>\n",
+       "      <td>11</td>\n",
+       "      <td>42</td>\n",
+       "      <td>44</td>\n",
+       "      <td>51</td>\n",
+       "      <td>18</td>\n",
+       "      <td>8</td>\n",
+       "      <td>12</td>\n",
+       "      <td>72</td>\n",
+       "      <td>53</td>\n",
+       "      <td>62</td>\n",
+       "      <td>12</td>\n",
+       "      <td>12</td>\n",
+       "      <td>14</td>\n",
+       "      <td>55</td>\n",
+       "      <td>62</td>\n",
+       "      <td>48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chelsea</th>\n",
+       "      <td>21</td>\n",
+       "      <td>11</td>\n",
        "      <td>6</td>\n",
-       "      <td>3</td>\n",
+       "      <td>76</td>\n",
+       "      <td>33</td>\n",
+       "      <td>74</td>\n",
+       "      <td>11</td>\n",
+       "      <td>11</td>\n",
+       "      <td>16</td>\n",
+       "      <td>38</td>\n",
+       "      <td>47</td>\n",
+       "      <td>44</td>\n",
+       "      <td>18</td>\n",
+       "      <td>9</td>\n",
+       "      <td>11</td>\n",
+       "      <td>77</td>\n",
+       "      <td>63</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Crystal Palace</th>\n",
+       "      <td>11</td>\n",
+       "      <td>15</td>\n",
+       "      <td>12</td>\n",
+       "      <td>50</td>\n",
+       "      <td>46</td>\n",
+       "      <td>48</td>\n",
+       "      <td>11</td>\n",
+       "      <td>12</td>\n",
+       "      <td>15</td>\n",
+       "      <td>40</td>\n",
+       "      <td>49</td>\n",
+       "      <td>45</td>\n",
+       "      <td>13</td>\n",
+       "      <td>10</td>\n",
+       "      <td>15</td>\n",
+       "      <td>57</td>\n",
+       "      <td>58</td>\n",
+       "      <td>49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Everton</th>\n",
+       "      <td>11</td>\n",
        "      <td>6</td>\n",
-       "      <td>7</td>\n",
+       "      <td>21</td>\n",
+       "      <td>43</td>\n",
+       "      <td>66</td>\n",
+       "      <td>39</td>\n",
+       "      <td>8</td>\n",
+       "      <td>12</td>\n",
+       "      <td>18</td>\n",
+       "      <td>34</td>\n",
+       "      <td>57</td>\n",
+       "      <td>36</td>\n",
+       "      <td>13</td>\n",
+       "      <td>9</td>\n",
+       "      <td>16</td>\n",
+       "      <td>40</td>\n",
+       "      <td>51</td>\n",
+       "      <td>40</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Wine 2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
+       "      <th>Liverpool</th>\n",
+       "      <td>28</td>\n",
+       "      <td>8</td>\n",
        "      <td>2</td>\n",
+       "      <td>94</td>\n",
+       "      <td>26</td>\n",
+       "      <td>92</td>\n",
+       "      <td>19</td>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>75</td>\n",
+       "      <td>47</td>\n",
+       "      <td>67</td>\n",
+       "      <td>24</td>\n",
+       "      <td>10</td>\n",
        "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
+       "      <td>86</td>\n",
+       "      <td>41</td>\n",
+       "      <td>82</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Wine 3</th>\n",
-       "      <td>2</td>\n",
+       "      <th>Manchester City</th>\n",
+       "      <td>29</td>\n",
        "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>99</td>\n",
+       "      <td>26</td>\n",
+       "      <td>93</td>\n",
+       "      <td>28</td>\n",
        "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>94</td>\n",
+       "      <td>33</td>\n",
+       "      <td>89</td>\n",
+       "      <td>28</td>\n",
        "      <td>7</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>96</td>\n",
+       "      <td>34</td>\n",
+       "      <td>91</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Wine 4</th>\n",
-       "      <td>2</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
+       "      <th>Manchester United</th>\n",
+       "      <td>16</td>\n",
+       "      <td>10</td>\n",
+       "      <td>12</td>\n",
+       "      <td>57</td>\n",
+       "      <td>57</td>\n",
+       "      <td>58</td>\n",
+       "      <td>23</td>\n",
+       "      <td>6</td>\n",
+       "      <td>9</td>\n",
+       "      <td>58</td>\n",
+       "      <td>43</td>\n",
+       "      <td>75</td>\n",
+       "      <td>18</td>\n",
+       "      <td>6</td>\n",
+       "      <td>14</td>\n",
+       "      <td>57</td>\n",
+       "      <td>58</td>\n",
+       "      <td>60</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Wine 5</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
+       "      <th>Newcastle United</th>\n",
+       "      <td>13</td>\n",
+       "      <td>10</td>\n",
+       "      <td>15</td>\n",
+       "      <td>44</td>\n",
+       "      <td>62</td>\n",
+       "      <td>49</td>\n",
+       "      <td>19</td>\n",
+       "      <td>14</td>\n",
        "      <td>5</td>\n",
+       "      <td>68</td>\n",
+       "      <td>33</td>\n",
+       "      <td>71</td>\n",
+       "      <td>18</td>\n",
        "      <td>6</td>\n",
+       "      <td>14</td>\n",
+       "      <td>85</td>\n",
+       "      <td>62</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tottenham Hotspur</th>\n",
+       "      <td>22</td>\n",
        "      <td>5</td>\n",
-       "      <td>2</td>\n",
+       "      <td>11</td>\n",
+       "      <td>69</td>\n",
+       "      <td>40</td>\n",
+       "      <td>71</td>\n",
+       "      <td>18</td>\n",
        "      <td>6</td>\n",
+       "      <td>14</td>\n",
+       "      <td>70</td>\n",
+       "      <td>63</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
        "      <td>6</td>\n",
+       "      <td>12</td>\n",
+       "      <td>74</td>\n",
+       "      <td>61</td>\n",
+       "      <td>66</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Wine 6</th>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
+       "      <th>West Ham United</th>\n",
+       "      <td>16</td>\n",
+       "      <td>8</td>\n",
+       "      <td>14</td>\n",
+       "      <td>60</td>\n",
+       "      <td>51</td>\n",
+       "      <td>56</td>\n",
+       "      <td>11</td>\n",
        "      <td>7</td>\n",
-       "      <td>5</td>\n",
+       "      <td>20</td>\n",
+       "      <td>42</td>\n",
+       "      <td>55</td>\n",
+       "      <td>40</td>\n",
+       "      <td>14</td>\n",
+       "      <td>10</td>\n",
+       "      <td>14</td>\n",
+       "      <td>60</td>\n",
+       "      <td>74</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Wolverhampton Wanderers</th>\n",
+       "      <td>15</td>\n",
+       "      <td>6</td>\n",
+       "      <td>17</td>\n",
+       "      <td>38</td>\n",
+       "      <td>43</td>\n",
+       "      <td>51</td>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "      <td>19</td>\n",
+       "      <td>31</td>\n",
+       "      <td>58</td>\n",
+       "      <td>41</td>\n",
+       "      <td>13</td>\n",
+       "      <td>7</td>\n",
+       "      <td>18</td>\n",
+       "      <td>50</td>\n",
+       "      <td>65</td>\n",
+       "      <td>46</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "expert Oak type Expert 1               Expert 2                          \n",
-       "aspect            Fruity Woody Coffee Red fruit Roasted Vanillin Woody   \n",
-       "Wine 1        1        1     6      7         2       5        7     6  \\\n",
-       "Wine 2        2        5     3      2         4       4        4     2   \n",
-       "Wine 3        2        6     1      1         5       2        1     1   \n",
-       "Wine 4        2        7     1      2         7       2        1     2   \n",
-       "Wine 5        1        2     5      4         3       5        6     5   \n",
-       "Wine 6        1        3     4      4         3       5        4     5   \n",
+       "                        2021-22                     2022-23                  \\\n",
+       "                              W   D   L  GF  GA Pts       W   D   L  GF  GA   \n",
+       "Team                                                                          \n",
+       "Arsenal                      22   3  13  61  48  69      26   6   6  88  43   \n",
+       "Aston Villa                  13   6  19  52  54  45      18   7  13  51  46   \n",
+       "Brentford                    13   7  18  48  56  46      15  14   9  58  46   \n",
+       "Brighton & Hove Albion       12  15  11  42  44  51      18   8  12  72  53   \n",
+       "Chelsea                      21  11   6  76  33  74      11  11  16  38  47   \n",
+       "Crystal Palace               11  15  12  50  46  48      11  12  15  40  49   \n",
+       "Everton                      11   6  21  43  66  39       8  12  18  34  57   \n",
+       "Liverpool                    28   8   2  94  26  92      19  10   9  75  47   \n",
+       "Manchester City              29   6   3  99  26  93      28   5   5  94  33   \n",
+       "Manchester United            16  10  12  57  57  58      23   6   9  58  43   \n",
+       "Newcastle United             13  10  15  44  62  49      19  14   5  68  33   \n",
+       "Tottenham Hotspur            22   5  11  69  40  71      18   6  14  70  63   \n",
+       "West Ham United              16   8  14  60  51  56      11   7  20  42  55   \n",
+       "Wolverhampton Wanderers      15   6  17  38  43  51      11   8  19  31  58   \n",
        "\n",
-       "expert Expert 3               \n",
-       "aspect   Fruity Butter Woody  \n",
-       "Wine 1        3      6     7  \n",
-       "Wine 2        4      4     3  \n",
-       "Wine 3        7      1     1  \n",
-       "Wine 4        2      2     2  \n",
-       "Wine 5        2      6     6  \n",
-       "Wine 6        1      7     5  "
+       "                            2023-24                      \n",
+       "                        Pts       W   D   L  GF  GA Pts  \n",
+       "Team                                                     \n",
+       "Arsenal                  84      28   5   5  91  29  89  \n",
+       "Aston Villa              61      20   8  10  76  61  68  \n",
+       "Brentford                59      10   9  19  56  65  39  \n",
+       "Brighton & Hove Albion   62      12  12  14  55  62  48  \n",
+       "Chelsea                  44      18   9  11  77  63  63  \n",
+       "Crystal Palace           45      13  10  15  57  58  49  \n",
+       "Everton                  36      13   9  16  40  51  40  \n",
+       "Liverpool                67      24  10   4  86  41  82  \n",
+       "Manchester City          89      28   7   3  96  34  91  \n",
+       "Manchester United        75      18   6  14  57  58  60  \n",
+       "Newcastle United         71      18   6  14  85  62  60  \n",
+       "Tottenham Hotspur        60      20   6  12  74  61  66  \n",
+       "West Ham United          40      14  10  14  60  74  52  \n",
+       "Wolverhampton Wanderers  41      13   7  18  50  65  46  "
       ]
      },
      "execution_count": 1,
@@ -210,12 +471,34 @@
     }
    ],
    "source": [
-    "import prince \n",
+    "import prince\n",
     "\n",
-    "dataset = prince.datasets.load_burgundy_wines()\n",
+    "dataset = prince.datasets.load_premier_league()\n",
     "dataset"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "isinstance(dataset.columns, pd.MultiIndex)"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -229,12 +512,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The groups are passed as a dictionary to the `fit` method."
+    "The groups are specified by the `groups` argument when calling `fit`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-09-07T18:18:07.158550Z",
@@ -247,22 +530,22 @@
     {
      "data": {
       "text/plain": [
-       "['Expert 1', 'Expert 2', 'Expert 3']"
+       "['2021-22', '2022-23', '2023-24']"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "groups = dataset.columns.levels[0].drop(\"Oak type\").tolist()\n",
+    "groups = dataset.columns.levels[0].tolist()\n",
     "groups"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-09-07T18:18:07.170307Z",
@@ -274,14 +557,32 @@
    "outputs": [],
    "source": [
     "mfa = prince.MFA(\n",
-    "    n_components=2,\n",
+    "    n_components=3,\n",
     "    n_iter=3,\n",
     "    copy=True,\n",
     "    check_input=True,\n",
     "    engine='sklearn',\n",
     "    random_state=42\n",
     ")\n",
-    "mfa = mfa.fit(dataset, groups=groups)"
+    "mfa = mfa.fit(\n",
+    "    dataset,\n",
+    "    groups=groups,\n",
+    "    supplementary_groups=None\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are several ways to specify the groups:\n",
+    "\n",
+    "- If the columns of the dataframe are a `MultiIndex`:\n",
+    "   - By default the groups are all the columns in the first level.\n",
+    "   - You can also pass a list with a subset of the columns in the first level.\n",
+    "- You can also pass a dict that maps group names to the desired columns.\n",
+    "\n",
+    "The `supplementary_groups` argument is expected to be a list with one more existing group names."
    ]
   },
   {
@@ -294,7 +595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-09-07T18:18:07.205973Z",
@@ -339,15 +640,21 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>2.835</td>\n",
-       "      <td>88.82%</td>\n",
-       "      <td>88.82%</td>\n",
+       "      <td>2.376</td>\n",
+       "      <td>59.53%</td>\n",
+       "      <td>59.53%</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.357</td>\n",
-       "      <td>11.18%</td>\n",
-       "      <td>100.00%</td>\n",
+       "      <td>0.619</td>\n",
+       "      <td>15.51%</td>\n",
+       "      <td>75.04%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.412</td>\n",
+       "      <td>10.32%</td>\n",
+       "      <td>85.36%</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -356,11 +663,12 @@
       "text/plain": [
        "          eigenvalue % of variance % of variance (cumulative)\n",
        "component                                                    \n",
-       "0              2.835        88.82%                     88.82%\n",
-       "1              0.357        11.18%                    100.00%"
+       "0              2.376        59.53%                     59.53%\n",
+       "1              0.619        15.51%                     75.04%\n",
+       "2              0.412        10.32%                     85.36%"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -387,7 +695,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-09-07T18:18:07.266647Z",
@@ -417,57 +725,127 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
+       "      <th>component</th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Team</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>Wine 1</th>\n",
-       "      <td>-2.172155</td>\n",
-       "      <td>-0.508596</td>\n",
+       "      <th>Arsenal</th>\n",
+       "      <td>2.236971</td>\n",
+       "      <td>1.034584</td>\n",
+       "      <td>0.697651</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Aston Villa</th>\n",
+       "      <td>-0.179988</td>\n",
+       "      <td>0.580297</td>\n",
+       "      <td>0.463962</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Brentford</th>\n",
+       "      <td>-1.267447</td>\n",
+       "      <td>0.696757</td>\n",
+       "      <td>-0.490607</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Brighton &amp; Hove Albion</th>\n",
+       "      <td>-0.800062</td>\n",
+       "      <td>-0.248918</td>\n",
+       "      <td>-0.904603</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Wine 2</th>\n",
-       "      <td>0.557017</td>\n",
-       "      <td>-0.197408</td>\n",
+       "      <th>Chelsea</th>\n",
+       "      <td>0.000108</td>\n",
+       "      <td>-1.253858</td>\n",
+       "      <td>-0.365442</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Wine 3</th>\n",
-       "      <td>2.317663</td>\n",
-       "      <td>-0.830259</td>\n",
+       "      <th>Crystal Palace</th>\n",
+       "      <td>-1.325908</td>\n",
+       "      <td>-0.410853</td>\n",
+       "      <td>-0.809261</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Wine 4</th>\n",
-       "      <td>1.832557</td>\n",
-       "      <td>0.905046</td>\n",
+       "      <th>Everton</th>\n",
+       "      <td>-2.089219</td>\n",
+       "      <td>0.184291</td>\n",
+       "      <td>0.552330</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Wine 5</th>\n",
-       "      <td>-1.403787</td>\n",
-       "      <td>0.054977</td>\n",
+       "      <th>Liverpool</th>\n",
+       "      <td>2.063236</td>\n",
+       "      <td>-1.170222</td>\n",
+       "      <td>-0.419547</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Wine 6</th>\n",
-       "      <td>-1.131296</td>\n",
-       "      <td>0.576241</td>\n",
+       "      <th>Manchester City</th>\n",
+       "      <td>3.393773</td>\n",
+       "      <td>-0.160572</td>\n",
+       "      <td>-0.151160</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Manchester United</th>\n",
+       "      <td>0.189448</td>\n",
+       "      <td>0.753614</td>\n",
+       "      <td>-0.007898</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Newcastle United</th>\n",
+       "      <td>-0.004656</td>\n",
+       "      <td>1.462421</td>\n",
+       "      <td>-0.872403</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tottenham Hotspur</th>\n",
+       "      <td>0.510562</td>\n",
+       "      <td>-0.416955</td>\n",
+       "      <td>0.992128</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>West Ham United</th>\n",
+       "      <td>-1.186842</td>\n",
+       "      <td>-0.756359</td>\n",
+       "      <td>0.432273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Wolverhampton Wanderers</th>\n",
+       "      <td>-1.539976</td>\n",
+       "      <td>-0.294226</td>\n",
+       "      <td>0.882576</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "               0         1\n",
-       "Wine 1 -2.172155 -0.508596\n",
-       "Wine 2  0.557017 -0.197408\n",
-       "Wine 3  2.317663 -0.830259\n",
-       "Wine 4  1.832557  0.905046\n",
-       "Wine 5 -1.403787  0.054977\n",
-       "Wine 6 -1.131296  0.576241"
+       "component                       0         1         2\n",
+       "Team                                                 \n",
+       "Arsenal                  2.236971  1.034584  0.697651\n",
+       "Aston Villa             -0.179988  0.580297  0.463962\n",
+       "Brentford               -1.267447  0.696757 -0.490607\n",
+       "Brighton & Hove Albion  -0.800062 -0.248918 -0.904603\n",
+       "Chelsea                  0.000108 -1.253858 -0.365442\n",
+       "Crystal Palace          -1.325908 -0.410853 -0.809261\n",
+       "Everton                 -2.089219  0.184291  0.552330\n",
+       "Liverpool                2.063236 -1.170222 -0.419547\n",
+       "Manchester City          3.393773 -0.160572 -0.151160\n",
+       "Manchester United        0.189448  0.753614 -0.007898\n",
+       "Newcastle United        -0.004656  1.462421 -0.872403\n",
+       "Tottenham Hotspur        0.510562 -0.416955  0.992128\n",
+       "West Ham United         -1.186842 -0.756359  0.432273\n",
+       "Wolverhampton Wanderers -1.539976 -0.294226  0.882576"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -477,16 +855,15 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "However, all the other methods are not implemented yet. They will raise a `NotImplemented` exception if you call them."
+    "There is also a `partial_row_coordinates` method that returns the coordinates projected onto each group."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-09-07T18:18:07.290866Z",
@@ -512,102 +889,262 @@
        "    .dataframe thead tr th {\n",
        "        text-align: left;\n",
        "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
-       "      <th>group</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Expert 1</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Expert 2</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Expert 3</th>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"3\" halign=\"left\">2021-22</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">2022-23</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">2023-24</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>component</th>\n",
+       "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
+       "      <th>2</th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
+       "      <th>2</th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Team</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>Wine 1</th>\n",
-       "      <td>-2.764432</td>\n",
-       "      <td>-1.104812</td>\n",
-       "      <td>-2.213928</td>\n",
-       "      <td>-0.863519</td>\n",
-       "      <td>-1.538106</td>\n",
-       "      <td>0.442545</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Wine 2</th>\n",
-       "      <td>0.773034</td>\n",
-       "      <td>0.298919</td>\n",
-       "      <td>0.284247</td>\n",
-       "      <td>-0.132135</td>\n",
-       "      <td>0.613771</td>\n",
-       "      <td>-0.759009</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Wine 3</th>\n",
-       "      <td>1.991398</td>\n",
-       "      <td>0.805893</td>\n",
-       "      <td>2.111508</td>\n",
-       "      <td>0.499718</td>\n",
-       "      <td>2.850084</td>\n",
-       "      <td>-3.796390</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Wine 4</th>\n",
-       "      <td>1.981456</td>\n",
-       "      <td>0.927187</td>\n",
-       "      <td>2.393009</td>\n",
-       "      <td>1.227146</td>\n",
-       "      <td>1.123206</td>\n",
-       "      <td>0.560803</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Wine 5</th>\n",
-       "      <td>-1.292834</td>\n",
-       "      <td>-0.620661</td>\n",
-       "      <td>-1.492114</td>\n",
-       "      <td>-0.488088</td>\n",
-       "      <td>-1.426414</td>\n",
-       "      <td>1.273679</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Wine 6</th>\n",
-       "      <td>-0.688623</td>\n",
-       "      <td>-0.306527</td>\n",
-       "      <td>-1.082723</td>\n",
-       "      <td>-0.243122</td>\n",
-       "      <td>-1.622541</td>\n",
-       "      <td>2.278372</td>\n",
+       "      <th>Arsenal</th>\n",
+       "      <td>0.690262</td>\n",
+       "      <td>-0.059517</td>\n",
+       "      <td>1.417084</td>\n",
+       "      <td>2.505624</td>\n",
+       "      <td>2.235689</td>\n",
+       "      <td>-0.825430</td>\n",
+       "      <td>3.515025</td>\n",
+       "      <td>0.927579</td>\n",
+       "      <td>1.501298</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Aston Villa</th>\n",
+       "      <td>-1.204890</td>\n",
+       "      <td>1.807432</td>\n",
+       "      <td>0.898128</td>\n",
+       "      <td>0.113710</td>\n",
+       "      <td>-0.035078</td>\n",
+       "      <td>0.371064</td>\n",
+       "      <td>0.551216</td>\n",
+       "      <td>-0.031464</td>\n",
+       "      <td>0.122694</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Brentford</th>\n",
+       "      <td>-1.289455</td>\n",
+       "      <td>1.825781</td>\n",
+       "      <td>0.620325</td>\n",
+       "      <td>-0.244223</td>\n",
+       "      <td>0.442700</td>\n",
+       "      <td>-1.365260</td>\n",
+       "      <td>-2.268664</td>\n",
+       "      <td>-0.178208</td>\n",
+       "      <td>-0.726887</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Brighton &amp; Hove Albion</th>\n",
+       "      <td>-1.025328</td>\n",
+       "      <td>0.230789</td>\n",
+       "      <td>-1.805521</td>\n",
+       "      <td>0.329520</td>\n",
+       "      <td>0.029076</td>\n",
+       "      <td>0.362772</td>\n",
+       "      <td>-1.704379</td>\n",
+       "      <td>-1.006619</td>\n",
+       "      <td>-1.271060</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chelsea</th>\n",
+       "      <td>1.423732</td>\n",
+       "      <td>-2.259632</td>\n",
+       "      <td>-1.063349</td>\n",
+       "      <td>-1.506446</td>\n",
+       "      <td>-1.230628</td>\n",
+       "      <td>0.235333</td>\n",
+       "      <td>0.083038</td>\n",
+       "      <td>-0.271314</td>\n",
+       "      <td>-0.268308</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Crystal Palace</th>\n",
+       "      <td>-1.106248</td>\n",
+       "      <td>0.364282</td>\n",
+       "      <td>-1.768677</td>\n",
+       "      <td>-1.512225</td>\n",
+       "      <td>-1.148029</td>\n",
+       "      <td>0.057866</td>\n",
+       "      <td>-1.359252</td>\n",
+       "      <td>-0.448812</td>\n",
+       "      <td>-0.716972</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Everton</th>\n",
+       "      <td>-2.025459</td>\n",
+       "      <td>3.013837</td>\n",
+       "      <td>1.068040</td>\n",
+       "      <td>-2.466096</td>\n",
+       "      <td>-2.297007</td>\n",
+       "      <td>1.002036</td>\n",
+       "      <td>-1.776102</td>\n",
+       "      <td>-0.163958</td>\n",
+       "      <td>-0.413086</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Liverpool</th>\n",
+       "      <td>3.136063</td>\n",
+       "      <td>-3.954644</td>\n",
+       "      <td>-0.494832</td>\n",
+       "      <td>0.796027</td>\n",
+       "      <td>0.895556</td>\n",
+       "      <td>-0.763894</td>\n",
+       "      <td>2.257618</td>\n",
+       "      <td>-0.451578</td>\n",
+       "      <td>0.000085</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Manchester City</th>\n",
+       "      <td>3.346269</td>\n",
+       "      <td>-3.936828</td>\n",
+       "      <td>0.058294</td>\n",
+       "      <td>3.304854</td>\n",
+       "      <td>3.094441</td>\n",
+       "      <td>-1.486358</td>\n",
+       "      <td>3.530198</td>\n",
+       "      <td>0.360672</td>\n",
+       "      <td>0.974585</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Manchester United</th>\n",
+       "      <td>-0.462376</td>\n",
+       "      <td>0.551069</td>\n",
+       "      <td>-0.388186</td>\n",
+       "      <td>1.322063</td>\n",
+       "      <td>1.191180</td>\n",
+       "      <td>-0.205701</td>\n",
+       "      <td>-0.291344</td>\n",
+       "      <td>0.518593</td>\n",
+       "      <td>0.570194</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Newcastle United</th>\n",
+       "      <td>-1.390156</td>\n",
+       "      <td>1.706830</td>\n",
+       "      <td>-0.225816</td>\n",
+       "      <td>1.136187</td>\n",
+       "      <td>2.110547</td>\n",
+       "      <td>-2.794385</td>\n",
+       "      <td>0.240001</td>\n",
+       "      <td>0.569887</td>\n",
+       "      <td>0.402993</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tottenham Hotspur</th>\n",
+       "      <td>1.098053</td>\n",
+       "      <td>-0.964328</td>\n",
+       "      <td>0.751364</td>\n",
+       "      <td>-0.037297</td>\n",
+       "      <td>-0.787417</td>\n",
+       "      <td>1.621485</td>\n",
+       "      <td>0.470930</td>\n",
+       "      <td>0.500881</td>\n",
+       "      <td>0.603535</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>West Ham United</th>\n",
+       "      <td>-0.343711</td>\n",
+       "      <td>0.524201</td>\n",
+       "      <td>0.161269</td>\n",
+       "      <td>-1.726248</td>\n",
+       "      <td>-2.191590</td>\n",
+       "      <td>1.896491</td>\n",
+       "      <td>-1.490567</td>\n",
+       "      <td>-0.601689</td>\n",
+       "      <td>-0.760941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Wolverhampton Wanderers</th>\n",
+       "      <td>-0.846757</td>\n",
+       "      <td>1.150731</td>\n",
+       "      <td>0.771878</td>\n",
+       "      <td>-2.015449</td>\n",
+       "      <td>-2.309439</td>\n",
+       "      <td>1.893981</td>\n",
+       "      <td>-1.757721</td>\n",
+       "      <td>0.276030</td>\n",
+       "      <td>-0.018129</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "group      Expert 1            Expert 2            Expert 3          \n",
-       "component         0         1         0         1         0         1\n",
-       "Wine 1    -2.764432 -1.104812 -2.213928 -0.863519 -1.538106  0.442545\n",
-       "Wine 2     0.773034  0.298919  0.284247 -0.132135  0.613771 -0.759009\n",
-       "Wine 3     1.991398  0.805893  2.111508  0.499718  2.850084 -3.796390\n",
-       "Wine 4     1.981456  0.927187  2.393009  1.227146  1.123206  0.560803\n",
-       "Wine 5    -1.292834 -0.620661 -1.492114 -0.488088 -1.426414  1.273679\n",
-       "Wine 6    -0.688623 -0.306527 -1.082723 -0.243122 -1.622541  2.278372"
+       "                          2021-22                       2022-23            \\\n",
+       "                                0         1         2         0         1   \n",
+       "Team                                                                        \n",
+       "Arsenal                  0.690262 -0.059517  1.417084  2.505624  2.235689   \n",
+       "Aston Villa             -1.204890  1.807432  0.898128  0.113710 -0.035078   \n",
+       "Brentford               -1.289455  1.825781  0.620325 -0.244223  0.442700   \n",
+       "Brighton & Hove Albion  -1.025328  0.230789 -1.805521  0.329520  0.029076   \n",
+       "Chelsea                  1.423732 -2.259632 -1.063349 -1.506446 -1.230628   \n",
+       "Crystal Palace          -1.106248  0.364282 -1.768677 -1.512225 -1.148029   \n",
+       "Everton                 -2.025459  3.013837  1.068040 -2.466096 -2.297007   \n",
+       "Liverpool                3.136063 -3.954644 -0.494832  0.796027  0.895556   \n",
+       "Manchester City          3.346269 -3.936828  0.058294  3.304854  3.094441   \n",
+       "Manchester United       -0.462376  0.551069 -0.388186  1.322063  1.191180   \n",
+       "Newcastle United        -1.390156  1.706830 -0.225816  1.136187  2.110547   \n",
+       "Tottenham Hotspur        1.098053 -0.964328  0.751364 -0.037297 -0.787417   \n",
+       "West Ham United         -0.343711  0.524201  0.161269 -1.726248 -2.191590   \n",
+       "Wolverhampton Wanderers -0.846757  1.150731  0.771878 -2.015449 -2.309439   \n",
+       "\n",
+       "                                    2023-24                      \n",
+       "                                2         0         1         2  \n",
+       "Team                                                             \n",
+       "Arsenal                 -0.825430  3.515025  0.927579  1.501298  \n",
+       "Aston Villa              0.371064  0.551216 -0.031464  0.122694  \n",
+       "Brentford               -1.365260 -2.268664 -0.178208 -0.726887  \n",
+       "Brighton & Hove Albion   0.362772 -1.704379 -1.006619 -1.271060  \n",
+       "Chelsea                  0.235333  0.083038 -0.271314 -0.268308  \n",
+       "Crystal Palace           0.057866 -1.359252 -0.448812 -0.716972  \n",
+       "Everton                  1.002036 -1.776102 -0.163958 -0.413086  \n",
+       "Liverpool               -0.763894  2.257618 -0.451578  0.000085  \n",
+       "Manchester City         -1.486358  3.530198  0.360672  0.974585  \n",
+       "Manchester United       -0.205701 -0.291344  0.518593  0.570194  \n",
+       "Newcastle United        -2.794385  0.240001  0.569887  0.402993  \n",
+       "Tottenham Hotspur        1.621485  0.470930  0.500881  0.603535  \n",
+       "West Ham United          1.896491 -1.490567 -0.601689 -0.760941  \n",
+       "Wolverhampton Wanderers  1.893981 -1.757721  0.276030 -0.018129  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "mfa.group_row_coordinates(dataset)"
+    "mfa.partial_row_coordinates(dataset)"
    ]
   },
   {
@@ -620,7 +1157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-09-07T18:18:07.324369Z",
@@ -634,19 +1171,31 @@
      "data": {
       "text/html": [
        "\n",
-       "<div id=\"altair-viz-d92c253af44d4d43b001c1af30e4f2cb\"></div>\n",
+       "<style>\n",
+       "  #altair-viz-0c6f5f91c0204855adbc199910cf616d.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-0c6f5f91c0204855adbc199910cf616d.vega-embed details,\n",
+       "  #altair-viz-0c6f5f91c0204855adbc199910cf616d.vega-embed details summary {\n",
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+       "</style>\n",
+       "<div id=\"altair-viz-0c6f5f91c0204855adbc199910cf616d\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
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-       "      outputDiv = document.getElementById(\"altair-viz-d92c253af44d4d43b001c1af30e4f2cb\");\n",
+       "    if (outputDiv.id !== \"altair-viz-0c6f5f91c0204855adbc199910cf616d\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-0c6f5f91c0204855adbc199910cf616d\");\n",
        "    }\n",
+       "\n",
        "    const paths = {\n",
-       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
-       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
-       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
-       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
        "    };\n",
        "\n",
        "    function maybeLoadScript(lib, version) {\n",
@@ -678,22 +1227,23 @@
        "\n",
        "    if(typeof define === \"function\" && define.amd) {\n",
        "      requirejs.config({paths});\n",
-       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
        "    } else {\n",
        "      maybeLoadScript(\"vega\", \"5\")\n",
-       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
        "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
        "        .catch(showError)\n",
        "        .then(() => displayChart(vegaEmbed));\n",
        "    }\n",
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        "</script>"
       ],
       "text/plain": [
-       "alt.Chart(...)"
+       "alt.LayerChart(...)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -711,7 +1261,102 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The first axis explains most of the difference between the wine ratings. This difference is actually due to the oak type of the barrels they were fermented in."
+    "The first axis explains most of the difference between the wine ratings. This difference is actually due to the oak type of the barrels they were fermented in.\n",
+    "\n",
+    "The `show_partial_rows` argument allows showing the global row coordinates together with the partial row coordinates. All the coordinates of each sample are connected with edges."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-09af977cc0ce428badab21ddc463cb11.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-09af977cc0ce428badab21ddc463cb11.vega-embed details,\n",
+       "  #altair-viz-09af977cc0ce428badab21ddc463cb11.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-09af977cc0ce428badab21ddc463cb11\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-09af977cc0ce428badab21ddc463cb11\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-09af977cc0ce428badab21ddc463cb11\");\n",
+       "    }\n",
+       "\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
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+       "        new Promise(function(resolve, reject) {\n",
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+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
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+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
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+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
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\"group\": \"2023-24\"}, {\"Team\": \"Wolverhampton Wanderers\", \"component 0_global\": -1.539975734171846, \"component 1_global\": -0.2942261371165038, \"component 0_partial\": -0.8467571135940937, \"component 1_partial\": 1.1507306143969163, \"group\": \"2021-22\"}, {\"Team\": \"Wolverhampton Wanderers\", \"component 0_global\": -1.539975734171846, \"component 1_global\": -0.2942261371165038, \"component 0_partial\": -2.015449461432138, \"component 1_partial\": -2.309439441267143, \"group\": \"2022-23\"}, {\"Team\": \"Wolverhampton Wanderers\", \"component 0_global\": -1.539975734171846, \"component 1_global\": -0.2942261371165038, \"component 0_partial\": -1.7577206274893062, \"component 1_partial\": 0.2760304155207144, \"group\": \"2023-24\"}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.LayerChart(...)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mfa.plot(\n",
+    "    dataset,\n",
+    "    show_partial_rows=True\n",
+    ")"
    ]
   },
   {
@@ -732,7 +1377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-09-07T18:18:07.395738Z",
@@ -762,75 +1407,186 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>aspect</th>\n",
-       "      <th>Fruity</th>\n",
-       "      <th>Woody</th>\n",
-       "      <th>Coffee</th>\n",
+       "      <th></th>\n",
+       "      <th>W</th>\n",
+       "      <th>D</th>\n",
+       "      <th>L</th>\n",
+       "      <th>GF</th>\n",
+       "      <th>GA</th>\n",
+       "      <th>Pts</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Team</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>Wine 1</th>\n",
-       "      <td>1</td>\n",
+       "      <th>Arsenal</th>\n",
+       "      <td>26</td>\n",
        "      <td>6</td>\n",
+       "      <td>6</td>\n",
+       "      <td>88</td>\n",
+       "      <td>43</td>\n",
+       "      <td>84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Aston Villa</th>\n",
+       "      <td>18</td>\n",
        "      <td>7</td>\n",
+       "      <td>13</td>\n",
+       "      <td>51</td>\n",
+       "      <td>46</td>\n",
+       "      <td>61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Brentford</th>\n",
+       "      <td>15</td>\n",
+       "      <td>14</td>\n",
+       "      <td>9</td>\n",
+       "      <td>58</td>\n",
+       "      <td>46</td>\n",
+       "      <td>59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Brighton &amp; Hove Albion</th>\n",
+       "      <td>18</td>\n",
+       "      <td>8</td>\n",
+       "      <td>12</td>\n",
+       "      <td>72</td>\n",
+       "      <td>53</td>\n",
+       "      <td>62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Wine 2</th>\n",
+       "      <th>Chelsea</th>\n",
+       "      <td>11</td>\n",
+       "      <td>11</td>\n",
+       "      <td>16</td>\n",
+       "      <td>38</td>\n",
+       "      <td>47</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Crystal Palace</th>\n",
+       "      <td>11</td>\n",
+       "      <td>12</td>\n",
+       "      <td>15</td>\n",
+       "      <td>40</td>\n",
+       "      <td>49</td>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Everton</th>\n",
+       "      <td>8</td>\n",
+       "      <td>12</td>\n",
+       "      <td>18</td>\n",
+       "      <td>34</td>\n",
+       "      <td>57</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Liverpool</th>\n",
+       "      <td>19</td>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>75</td>\n",
+       "      <td>47</td>\n",
+       "      <td>67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Manchester City</th>\n",
+       "      <td>28</td>\n",
        "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>94</td>\n",
+       "      <td>33</td>\n",
+       "      <td>89</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Wine 3</th>\n",
+       "      <th>Manchester United</th>\n",
+       "      <td>23</td>\n",
        "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
+       "      <td>9</td>\n",
+       "      <td>58</td>\n",
+       "      <td>43</td>\n",
+       "      <td>75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Wine 4</th>\n",
-       "      <td>7</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
+       "      <th>Newcastle United</th>\n",
+       "      <td>19</td>\n",
+       "      <td>14</td>\n",
+       "      <td>5</td>\n",
+       "      <td>68</td>\n",
+       "      <td>33</td>\n",
+       "      <td>71</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Wine 5</th>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
+       "      <th>Tottenham Hotspur</th>\n",
+       "      <td>18</td>\n",
+       "      <td>6</td>\n",
+       "      <td>14</td>\n",
+       "      <td>70</td>\n",
+       "      <td>63</td>\n",
+       "      <td>60</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Wine 6</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
+       "      <th>West Ham United</th>\n",
+       "      <td>11</td>\n",
+       "      <td>7</td>\n",
+       "      <td>20</td>\n",
+       "      <td>42</td>\n",
+       "      <td>55</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Wolverhampton Wanderers</th>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "      <td>19</td>\n",
+       "      <td>31</td>\n",
+       "      <td>58</td>\n",
+       "      <td>41</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "aspect  Fruity  Woody  Coffee\n",
-       "Wine 1       1      6       7\n",
-       "Wine 2       5      3       2\n",
-       "Wine 3       6      1       1\n",
-       "Wine 4       7      1       2\n",
-       "Wine 5       2      5       4\n",
-       "Wine 6       3      4       4"
+       "                          W   D   L  GF  GA  Pts\n",
+       "Team                                            \n",
+       "Arsenal                  26   6   6  88  43   84\n",
+       "Aston Villa              18   7  13  51  46   61\n",
+       "Brentford                15  14   9  58  46   59\n",
+       "Brighton & Hove Albion   18   8  12  72  53   62\n",
+       "Chelsea                  11  11  16  38  47   44\n",
+       "Crystal Palace           11  12  15  40  49   45\n",
+       "Everton                   8  12  18  34  57   36\n",
+       "Liverpool                19  10   9  75  47   67\n",
+       "Manchester City          28   5   5  94  33   89\n",
+       "Manchester United        23   6   9  58  43   75\n",
+       "Newcastle United         19  14   5  68  33   71\n",
+       "Tottenham Hotspur        18   6  14  70  63   60\n",
+       "West Ham United          11   7  20  42  55   40\n",
+       "Wolverhampton Wanderers  11   8  19  31  58   41"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "dataset['Expert 1']"
+    "dataset['2022-23']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 21,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-09-07T18:18:07.419686Z",
@@ -875,15 +1631,21 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>2.863</td>\n",
-       "      <td>95.42%</td>\n",
-       "      <td>95.42%</td>\n",
+       "      <td>4.374</td>\n",
+       "      <td>72.89%</td>\n",
+       "      <td>72.89%</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.120</td>\n",
-       "      <td>3.99%</td>\n",
-       "      <td>99.41%</td>\n",
+       "      <td>1.245</td>\n",
+       "      <td>20.74%</td>\n",
+       "      <td>93.64%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.320</td>\n",
+       "      <td>5.34%</td>\n",
+       "      <td>98.97%</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -892,23 +1654,24 @@
       "text/plain": [
        "          eigenvalue % of variance % of variance (cumulative)\n",
        "component                                                    \n",
-       "0              2.863        95.42%                     95.42%\n",
-       "1              0.120         3.99%                     99.41%"
+       "0              4.374        72.89%                     72.89%\n",
+       "1              1.245        20.74%                     93.64%\n",
+       "2              0.320         5.34%                     98.97%"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "mfa['Expert 1'].eigenvalues_summary"
+    "mfa['2022-23'].eigenvalues_summary"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "prince-NQ1O93Uh-py3.13",
    "language": "python",
    "name": "python3"
   },
@@ -922,12 +1685,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "441c2ec70d9faeb70e7723f55150c6260f4a26a9c828b90915d3399002e14f43"
-   }
+   "version": "3.13.0"
   }
  },
  "nbformat": 4,
diff --git a/docs/content/pca.ipynb b/docs/content/pca.ipynb
index 7d4d40b5..71df4efd 100644
--- a/docs/content/pca.ipynb
+++ b/docs/content/pca.ipynb
@@ -55,19 +55,19 @@
       "text/html": [
        "<style type=\"text/css\">\n",
        "</style>\n",
-       "<table id=\"T_e4a16\">\n",
+       "<table id=\"T_6aadf\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank\" >&nbsp;</th>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_e4a16_level0_col0\" class=\"col_heading level0 col0\" >coal</th>\n",
-       "      <th id=\"T_e4a16_level0_col1\" class=\"col_heading level0 col1\" >oil</th>\n",
-       "      <th id=\"T_e4a16_level0_col2\" class=\"col_heading level0 col2\" >gas</th>\n",
-       "      <th id=\"T_e4a16_level0_col3\" class=\"col_heading level0 col3\" >nuclear</th>\n",
-       "      <th id=\"T_e4a16_level0_col4\" class=\"col_heading level0 col4\" >hydro</th>\n",
-       "      <th id=\"T_e4a16_level0_col5\" class=\"col_heading level0 col5\" >wind</th>\n",
-       "      <th id=\"T_e4a16_level0_col6\" class=\"col_heading level0 col6\" >solar</th>\n",
-       "      <th id=\"T_e4a16_level0_col7\" class=\"col_heading level0 col7\" >other renewables</th>\n",
+       "      <th id=\"T_6aadf_level0_col0\" class=\"col_heading level0 col0\" >coal</th>\n",
+       "      <th id=\"T_6aadf_level0_col1\" class=\"col_heading level0 col1\" >oil</th>\n",
+       "      <th id=\"T_6aadf_level0_col2\" class=\"col_heading level0 col2\" >gas</th>\n",
+       "      <th id=\"T_6aadf_level0_col3\" class=\"col_heading level0 col3\" >nuclear</th>\n",
+       "      <th id=\"T_6aadf_level0_col4\" class=\"col_heading level0 col4\" >hydro</th>\n",
+       "      <th id=\"T_6aadf_level0_col5\" class=\"col_heading level0 col5\" >wind</th>\n",
+       "      <th id=\"T_6aadf_level0_col6\" class=\"col_heading level0 col6\" >solar</th>\n",
+       "      <th id=\"T_6aadf_level0_col7\" class=\"col_heading level0 col7\" >other renewables</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th class=\"index_name level0\" >continent</th>\n",
@@ -84,70 +84,70 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_e4a16_level0_row0\" class=\"row_heading level0 row0\" >Africa</th>\n",
-       "      <th id=\"T_e4a16_level1_row0\" class=\"row_heading level1 row0\" >Algeria</th>\n",
-       "      <td id=\"T_e4a16_row0_col0\" class=\"data row0 col0\" >1%</td>\n",
-       "      <td id=\"T_e4a16_row0_col1\" class=\"data row0 col1\" >35%</td>\n",
-       "      <td id=\"T_e4a16_row0_col2\" class=\"data row0 col2\" >64%</td>\n",
-       "      <td id=\"T_e4a16_row0_col3\" class=\"data row0 col3\" >0%</td>\n",
-       "      <td id=\"T_e4a16_row0_col4\" class=\"data row0 col4\" >0%</td>\n",
-       "      <td id=\"T_e4a16_row0_col5\" class=\"data row0 col5\" >0%</td>\n",
-       "      <td id=\"T_e4a16_row0_col6\" class=\"data row0 col6\" >0%</td>\n",
-       "      <td id=\"T_e4a16_row0_col7\" class=\"data row0 col7\" >0%</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_e4a16_level0_row1\" class=\"row_heading level0 row1\" >South America</th>\n",
-       "      <th id=\"T_e4a16_level1_row1\" class=\"row_heading level1 row1\" >Argentina</th>\n",
-       "      <td id=\"T_e4a16_row1_col0\" class=\"data row1 col0\" >1%</td>\n",
-       "      <td id=\"T_e4a16_row1_col1\" class=\"data row1 col1\" >35%</td>\n",
-       "      <td id=\"T_e4a16_row1_col2\" class=\"data row1 col2\" >50%</td>\n",
-       "      <td id=\"T_e4a16_row1_col3\" class=\"data row1 col3\" >2%</td>\n",
-       "      <td id=\"T_e4a16_row1_col4\" class=\"data row1 col4\" >10%</td>\n",
-       "      <td id=\"T_e4a16_row1_col5\" class=\"data row1 col5\" >1%</td>\n",
-       "      <td id=\"T_e4a16_row1_col6\" class=\"data row1 col6\" >0%</td>\n",
-       "      <td id=\"T_e4a16_row1_col7\" class=\"data row1 col7\" >1%</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_e4a16_level0_row2\" class=\"row_heading level0 row2\" >Oceania</th>\n",
-       "      <th id=\"T_e4a16_level1_row2\" class=\"row_heading level1 row2\" >Australia</th>\n",
-       "      <td id=\"T_e4a16_row2_col0\" class=\"data row2 col0\" >28%</td>\n",
-       "      <td id=\"T_e4a16_row2_col1\" class=\"data row2 col1\" >34%</td>\n",
-       "      <td id=\"T_e4a16_row2_col2\" class=\"data row2 col2\" >30%</td>\n",
-       "      <td id=\"T_e4a16_row2_col3\" class=\"data row2 col3\" >0%</td>\n",
-       "      <td id=\"T_e4a16_row2_col4\" class=\"data row2 col4\" >2%</td>\n",
-       "      <td id=\"T_e4a16_row2_col5\" class=\"data row2 col5\" >3%</td>\n",
-       "      <td id=\"T_e4a16_row2_col6\" class=\"data row2 col6\" >3%</td>\n",
-       "      <td id=\"T_e4a16_row2_col7\" class=\"data row2 col7\" >1%</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_e4a16_level0_row3\" class=\"row_heading level0 row3\" >Europe</th>\n",
-       "      <th id=\"T_e4a16_level1_row3\" class=\"row_heading level1 row3\" >Austria</th>\n",
-       "      <td id=\"T_e4a16_row3_col0\" class=\"data row3 col0\" >9%</td>\n",
-       "      <td id=\"T_e4a16_row3_col1\" class=\"data row3 col1\" >37%</td>\n",
-       "      <td id=\"T_e4a16_row3_col2\" class=\"data row3 col2\" >22%</td>\n",
-       "      <td id=\"T_e4a16_row3_col3\" class=\"data row3 col3\" >0%</td>\n",
-       "      <td id=\"T_e4a16_row3_col4\" class=\"data row3 col4\" >25%</td>\n",
-       "      <td id=\"T_e4a16_row3_col5\" class=\"data row3 col5\" >4%</td>\n",
-       "      <td id=\"T_e4a16_row3_col6\" class=\"data row3 col6\" >1%</td>\n",
-       "      <td id=\"T_e4a16_row3_col7\" class=\"data row3 col7\" >3%</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_e4a16_level0_row4\" class=\"row_heading level0 row4\" >Asia</th>\n",
-       "      <th id=\"T_e4a16_level1_row4\" class=\"row_heading level1 row4\" >Azerbaijan</th>\n",
-       "      <td id=\"T_e4a16_row4_col0\" class=\"data row4 col0\" >0%</td>\n",
-       "      <td id=\"T_e4a16_row4_col1\" class=\"data row4 col1\" >33%</td>\n",
-       "      <td id=\"T_e4a16_row4_col2\" class=\"data row4 col2\" >65%</td>\n",
-       "      <td id=\"T_e4a16_row4_col3\" class=\"data row4 col3\" >0%</td>\n",
-       "      <td id=\"T_e4a16_row4_col4\" class=\"data row4 col4\" >2%</td>\n",
-       "      <td id=\"T_e4a16_row4_col5\" class=\"data row4 col5\" >0%</td>\n",
-       "      <td id=\"T_e4a16_row4_col6\" class=\"data row4 col6\" >0%</td>\n",
-       "      <td id=\"T_e4a16_row4_col7\" class=\"data row4 col7\" >0%</td>\n",
+       "      <th id=\"T_6aadf_level0_row0\" class=\"row_heading level0 row0\" >Africa</th>\n",
+       "      <th id=\"T_6aadf_level1_row0\" class=\"row_heading level1 row0\" >Algeria</th>\n",
+       "      <td id=\"T_6aadf_row0_col0\" class=\"data row0 col0\" >1%</td>\n",
+       "      <td id=\"T_6aadf_row0_col1\" class=\"data row0 col1\" >35%</td>\n",
+       "      <td id=\"T_6aadf_row0_col2\" class=\"data row0 col2\" >64%</td>\n",
+       "      <td id=\"T_6aadf_row0_col3\" class=\"data row0 col3\" >0%</td>\n",
+       "      <td id=\"T_6aadf_row0_col4\" class=\"data row0 col4\" >0%</td>\n",
+       "      <td id=\"T_6aadf_row0_col5\" class=\"data row0 col5\" >0%</td>\n",
+       "      <td id=\"T_6aadf_row0_col6\" class=\"data row0 col6\" >0%</td>\n",
+       "      <td id=\"T_6aadf_row0_col7\" class=\"data row0 col7\" >0%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6aadf_level0_row1\" class=\"row_heading level0 row1\" >South America</th>\n",
+       "      <th id=\"T_6aadf_level1_row1\" class=\"row_heading level1 row1\" >Argentina</th>\n",
+       "      <td id=\"T_6aadf_row1_col0\" class=\"data row1 col0\" >1%</td>\n",
+       "      <td id=\"T_6aadf_row1_col1\" class=\"data row1 col1\" >35%</td>\n",
+       "      <td id=\"T_6aadf_row1_col2\" class=\"data row1 col2\" >50%</td>\n",
+       "      <td id=\"T_6aadf_row1_col3\" class=\"data row1 col3\" >2%</td>\n",
+       "      <td id=\"T_6aadf_row1_col4\" class=\"data row1 col4\" >10%</td>\n",
+       "      <td id=\"T_6aadf_row1_col5\" class=\"data row1 col5\" >1%</td>\n",
+       "      <td id=\"T_6aadf_row1_col6\" class=\"data row1 col6\" >0%</td>\n",
+       "      <td id=\"T_6aadf_row1_col7\" class=\"data row1 col7\" >1%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6aadf_level0_row2\" class=\"row_heading level0 row2\" >Oceania</th>\n",
+       "      <th id=\"T_6aadf_level1_row2\" class=\"row_heading level1 row2\" >Australia</th>\n",
+       "      <td id=\"T_6aadf_row2_col0\" class=\"data row2 col0\" >28%</td>\n",
+       "      <td id=\"T_6aadf_row2_col1\" class=\"data row2 col1\" >34%</td>\n",
+       "      <td id=\"T_6aadf_row2_col2\" class=\"data row2 col2\" >30%</td>\n",
+       "      <td id=\"T_6aadf_row2_col3\" class=\"data row2 col3\" >0%</td>\n",
+       "      <td id=\"T_6aadf_row2_col4\" class=\"data row2 col4\" >2%</td>\n",
+       "      <td id=\"T_6aadf_row2_col5\" class=\"data row2 col5\" >3%</td>\n",
+       "      <td id=\"T_6aadf_row2_col6\" class=\"data row2 col6\" >3%</td>\n",
+       "      <td id=\"T_6aadf_row2_col7\" class=\"data row2 col7\" >1%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6aadf_level0_row3\" class=\"row_heading level0 row3\" >Europe</th>\n",
+       "      <th id=\"T_6aadf_level1_row3\" class=\"row_heading level1 row3\" >Austria</th>\n",
+       "      <td id=\"T_6aadf_row3_col0\" class=\"data row3 col0\" >9%</td>\n",
+       "      <td id=\"T_6aadf_row3_col1\" class=\"data row3 col1\" >37%</td>\n",
+       "      <td id=\"T_6aadf_row3_col2\" class=\"data row3 col2\" >22%</td>\n",
+       "      <td id=\"T_6aadf_row3_col3\" class=\"data row3 col3\" >0%</td>\n",
+       "      <td id=\"T_6aadf_row3_col4\" class=\"data row3 col4\" >25%</td>\n",
+       "      <td id=\"T_6aadf_row3_col5\" class=\"data row3 col5\" >4%</td>\n",
+       "      <td id=\"T_6aadf_row3_col6\" class=\"data row3 col6\" >1%</td>\n",
+       "      <td id=\"T_6aadf_row3_col7\" class=\"data row3 col7\" >3%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6aadf_level0_row4\" class=\"row_heading level0 row4\" >Asia</th>\n",
+       "      <th id=\"T_6aadf_level1_row4\" class=\"row_heading level1 row4\" >Azerbaijan</th>\n",
+       "      <td id=\"T_6aadf_row4_col0\" class=\"data row4 col0\" >0%</td>\n",
+       "      <td id=\"T_6aadf_row4_col1\" class=\"data row4 col1\" >33%</td>\n",
+       "      <td id=\"T_6aadf_row4_col2\" class=\"data row4 col2\" >65%</td>\n",
+       "      <td id=\"T_6aadf_row4_col3\" class=\"data row4 col3\" >0%</td>\n",
+       "      <td id=\"T_6aadf_row4_col4\" class=\"data row4 col4\" >2%</td>\n",
+       "      <td id=\"T_6aadf_row4_col5\" class=\"data row4 col5\" >0%</td>\n",
+       "      <td id=\"T_6aadf_row4_col6\" class=\"data row4 col6\" >0%</td>\n",
+       "      <td id=\"T_6aadf_row4_col7\" class=\"data row4 col7\" >0%</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x1243c70d0>"
+       "<pandas.io.formats.style.Styler at 0x1516daf90>"
       ]
      },
      "execution_count": 1,
@@ -159,7 +159,7 @@
     "import prince\n",
     "\n",
     "dataset = prince.datasets.load_energy_mix(year=2019, normalize=True)\n",
-    "dataset.head().style.format('{:.0%}')  "
+    "dataset.head().style.format('{:.0%}')"
    ]
   },
   {
@@ -201,7 +201,12 @@
     "    engine='sklearn',\n",
     "    random_state=42\n",
     ")\n",
-    "pca = pca.fit(dataset)"
+    "pca = pca.fit(\n",
+    "    dataset,\n",
+    "    sample_weight=None,\n",
+    "    column_weight=None,\n",
+    "    supplementary_columns=None\n",
+    ")"
    ]
   },
   {
@@ -419,7 +424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-09-07T18:18:09.427611Z",
@@ -433,19 +438,31 @@
      "data": {
       "text/html": [
        "\n",
-       "<div id=\"altair-viz-4325d79b7f0e4025907f37da65b9c41a\"></div>\n",
+       "<style>\n",
+       "  #altair-viz-fe0664427ad54920b27f1e1626e6a351.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-fe0664427ad54920b27f1e1626e6a351.vega-embed details,\n",
+       "  #altair-viz-fe0664427ad54920b27f1e1626e6a351.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-fe0664427ad54920b27f1e1626e6a351\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-4325d79b7f0e4025907f37da65b9c41a\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-4325d79b7f0e4025907f37da65b9c41a\");\n",
+       "    if (outputDiv.id !== \"altair-viz-fe0664427ad54920b27f1e1626e6a351\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-fe0664427ad54920b27f1e1626e6a351\");\n",
        "    }\n",
+       "\n",
        "    const paths = {\n",
-       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
-       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
-       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
-       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
        "    };\n",
        "\n",
        "    function maybeLoadScript(lib, version) {\n",
@@ -477,22 +494,23 @@
        "\n",
        "    if(typeof define === \"function\" && define.amd) {\n",
        "      requirejs.config({paths});\n",
-       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
        "    } else {\n",
        "      maybeLoadScript(\"vega\", \"5\")\n",
-       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
        "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
        "        .catch(showError)\n",
        "        .then(() => displayChart(vegaEmbed));\n",
        "    }\n",
-       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-f8257e54d7f625c58dcf5721833c085b\"}, \"mark\": {\"type\": \"bar\", \"size\": 10}, \"encoding\": {\"tooltip\": [{\"field\": \"component\", \"type\": \"nominal\"}, {\"field\": \"eigenvalue\", \"type\": \"quantitative\"}, {\"field\": \"% of variance\", \"type\": \"quantitative\"}, {\"field\": \"% of variance (cumulative)\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"component\", \"type\": \"nominal\"}, \"y\": {\"field\": \"eigenvalue\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-f8257e54d7f625c58dcf5721833c085b\": [{\"component\": \"0\", \"eigenvalue\": 1.9630121441911244, \"% of variance\": 24.537651802389053, \"% of variance (cumulative)\": 24.537651802389053}, {\"component\": \"1\", \"eigenvalue\": 1.5606998579915567, \"% of variance\": 19.50874822489446, \"% of variance (cumulative)\": 44.04640002728351}, {\"component\": \"2\", \"eigenvalue\": 1.4032782280687053, \"% of variance\": 17.540977850858816, \"% of variance (cumulative)\": 61.58737787814233}]}}, {\"mode\": \"vega-lite\"});\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-fb982e5c88c6242d699547a8100b6579\"}, \"mark\": {\"type\": \"bar\", \"size\": 10}, \"encoding\": {\"tooltip\": [{\"field\": \"component\", \"type\": \"nominal\"}, {\"field\": \"eigenvalue\", \"type\": \"quantitative\"}, {\"field\": \"% of variance\", \"type\": \"quantitative\"}, {\"field\": \"% of variance (cumulative)\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"component\", \"type\": \"nominal\"}, \"y\": {\"field\": \"eigenvalue\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.20.1.json\", \"datasets\": {\"data-fb982e5c88c6242d699547a8100b6579\": [{\"component\": \"0\", \"eigenvalue\": 1.9630121441911244, \"% of variance\": 24.53765180238906, \"% of variance (cumulative)\": 24.53765180238906}, {\"component\": \"1\", \"eigenvalue\": 1.5606998579915585, \"% of variance\": 19.50874822489449, \"% of variance (cumulative)\": 44.04640002728355}, {\"component\": \"2\", \"eigenvalue\": 1.403278228068708, \"% of variance\": 17.540977850858855, \"% of variance (cumulative)\": 61.587377878142405}]}}, {\"mode\": \"vega-lite\"});\n",
        "</script>"
       ],
       "text/plain": [
        "alt.Chart(...)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -513,7 +531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-09-07T18:18:09.478918Z",
@@ -607,7 +625,7 @@
        "Asia          Azerbaijan -2.190535  0.632250 -0.365515"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -626,7 +644,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-09-07T18:18:09.506575Z",
@@ -642,7 +660,7 @@
        "True"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -669,7 +687,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-09-07T18:18:09.529498Z",
@@ -777,7 +795,7 @@
        "other renewables  0.628750  0.516935 -0.084114"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -804,7 +822,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-09-07T18:18:09.546954Z",
@@ -818,19 +836,31 @@
      "data": {
       "text/html": [
        "\n",
-       "<div id=\"altair-viz-675dfcc0a0c9445aa12eef272258c4eb\"></div>\n",
+       "<style>\n",
+       "  #altair-viz-32580f15d5bb4336be074e5b7f167642.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-32580f15d5bb4336be074e5b7f167642.vega-embed details,\n",
+       "  #altair-viz-32580f15d5bb4336be074e5b7f167642.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-32580f15d5bb4336be074e5b7f167642\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-675dfcc0a0c9445aa12eef272258c4eb\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-675dfcc0a0c9445aa12eef272258c4eb\");\n",
+       "    if (outputDiv.id !== \"altair-viz-32580f15d5bb4336be074e5b7f167642\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-32580f15d5bb4336be074e5b7f167642\");\n",
        "    }\n",
+       "\n",
        "    const paths = {\n",
-       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
-       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
-       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
-       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
        "    };\n",
        "\n",
        "    function maybeLoadScript(lib, version) {\n",
@@ -862,22 +892,23 @@
        "\n",
        "    if(typeof define === \"function\" && define.amd) {\n",
        "      requirejs.config({paths});\n",
-       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "      let deps = [\"vega-embed\"];\n",
+       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
        "    } else {\n",
        "      maybeLoadScript(\"vega\", \"5\")\n",
-       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
        "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
        "        .catch(showError)\n",
        "        .then(() => displayChart(vegaEmbed));\n",
        "    }\n",
-       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"layer\": [{\"data\": {\"name\": \"data-9dedb64747da29494f16a7f391aeeeed\"}, \"mark\": {\"type\": \"circle\", \"size\": 50}, \"encoding\": {\"color\": {\"field\": \"continent\", \"type\": \"nominal\"}, \"tooltip\": [{\"field\": \"continent\", \"type\": \"nominal\"}, {\"field\": \"country\", \"type\": \"nominal\"}, {\"field\": \"component 0\", \"type\": \"quantitative\"}, {\"field\": \"component 1\", \"type\": \"quantitative\"}], \"x\": {\"axis\": {\"title\": \"component 0 \\u2014 24.54%\"}, \"field\": \"component 0\", \"scale\": {\"zero\": false}, \"type\": \"quantitative\"}, \"y\": {\"axis\": {\"title\": \"component 1 \\u2014 19.51%\"}, \"field\": \"component 1\", \"scale\": {\"zero\": false}, \"type\": \"quantitative\"}}, \"selection\": {\"selector001\": {\"type\": \"interval\", \"bind\": \"scales\", \"encodings\": [\"x\", \"y\"]}}}, {\"data\": {\"name\": 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0.20396903697829996, \"component 1\": -1.4702539632688774, \"component 2\": 0.5318189014543291, \"label\": \"Oceania\"}, {\"continent\": \"Europe\", \"country\": \"Austria\", \"component 0\": 0.8471216351969479, \"component 1\": 1.0082962135958795, \"component 2\": -0.5219975517003427, \"label\": \"Europe\"}, {\"continent\": \"Asia\", \"country\": \"Azerbaijan\", \"component 0\": -2.1905347892766986, \"component 1\": 0.6322502201648145, \"component 2\": -0.3655152611379298, \"label\": \"Asia\"}, {\"continent\": \"Asia\", \"country\": \"Bangladesh\", \"component 0\": -2.3971270086303997, \"component 1\": 0.5978805199941839, \"component 2\": 0.40469847963659356, \"label\": \"Asia\"}, {\"continent\": \"Europe\", \"country\": \"Belarus\", \"component 0\": -2.249012696615155, \"component 1\": 0.5130378176751521, \"component 2\": -0.11468609899301062, \"label\": \"Europe\"}, {\"continent\": \"Europe\", \"country\": \"Belgium\", \"component 0\": 0.5129552509797376, \"component 1\": -0.791852539948514, \"component 2\": -0.40080513356150993, \"label\": \"Europe\"}, {\"continent\": \"South America\", \"country\": \"Brazil\", \"component 0\": 1.4326021939469042, \"component 1\": 1.6174697914842988, \"component 2\": -0.7258786903436136, \"label\": \"South America\"}, {\"continent\": \"Europe\", \"country\": \"Bulgaria\", \"component 0\": 1.1854225672961234, \"component 1\": -1.0657556131268573, \"component 2\": 1.7702403719829174, \"label\": \"Europe\"}, {\"continent\": \"North America\", \"country\": \"Canada\", \"component 0\": -0.04935611938254241, \"component 1\": 1.331451681684089, \"component 2\": 0.018472770393809842, \"label\": \"North America\"}, {\"continent\": \"South America\", \"country\": \"Chile\", \"component 0\": 1.8701488621321474, \"component 1\": -1.0171233793722618, \"component 2\": -0.40742324451367845, \"label\": \"South America\"}, {\"continent\": \"Asia\", \"country\": \"China\", \"component 0\": 0.8976977193613275, \"component 1\": -0.9862977117676451, \"component 2\": 2.3639948820355214, \"label\": \"Asia\"}, {\"continent\": \"South America\", \"country\": \"Colombia\", \"component 0\": -0.21546953617959372, \"component 1\": 1.2353495946295192, \"component 2\": 0.10620523150314583, \"label\": \"South America\"}, {\"continent\": \"Europe\", \"country\": \"Croatia\", \"component 0\": -0.02605145097070807, \"component 1\": 0.8354359363062139, \"component 2\": -0.774255397318508, \"label\": \"Europe\"}, {\"continent\": \"Asia\", \"country\": \"Cyprus\", \"component 0\": 0.8687868577482017, \"component 1\": -2.089658829543805, \"component 2\": -2.695608316196076, \"label\": \"Asia\"}, {\"continent\": \"Europe\", \"country\": \"Czechia\", \"component 0\": 0.6274389636722525, \"component 1\": -0.5459233709324992, \"component 2\": 2.071009507937304, \"label\": \"Europe\"}, {\"continent\": \"Europe\", \"country\": \"Denmark\", \"component 0\": 3.095489636399334, \"component 1\": -0.6040755184733358, \"component 2\": 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-0.4743396521345639, \"label\": \"Africa\"}, {\"continent\": \"Europe\", \"country\": \"Netherlands\", \"component 0\": -0.3677161721064314, \"component 1\": -0.700778674056853, \"component 2\": -0.7817802429951333, \"label\": \"Europe\"}, {\"continent\": \"Oceania\", \"country\": \"New Zealand\", \"component 0\": 1.1874655793567375, \"component 1\": 2.1067984945773763, \"component 2\": -0.5611492015188742, \"label\": \"Oceania\"}, {\"continent\": \"Europe\", \"country\": \"North Macedonia\", \"component 0\": 0.20630503558060723, \"component 1\": -0.342937447916842, \"component 2\": 0.9265787267421638, \"label\": \"Europe\"}, {\"continent\": \"Europe\", \"country\": \"Norway\", \"component 0\": 1.2610276286722437, \"component 1\": 3.4343463147349054, \"component 2\": 0.029857176579483363, \"label\": \"Europe\"}, {\"continent\": \"Asia\", \"country\": \"Oman\", \"component 0\": -2.0859852511976067, \"component 1\": 0.35069242163176767, \"component 2\": -0.5926625994332709, \"label\": \"Asia\"}, {\"continent\": \"Asia\", \"country\": \"Pakistan\", \"component 0\": -1.1124312900847928, \"component 1\": 0.5706273023800144, \"component 2\": 0.5735798414309806, \"label\": \"Asia\"}, {\"continent\": \"South America\", \"country\": \"Peru\", \"component 0\": 0.07634396589763001, \"component 1\": 1.0433687069166266, \"component 2\": -0.7441762552237493, \"label\": \"South America\"}, {\"continent\": \"Asia\", \"country\": \"Philippines\", \"component 0\": 0.8894342072409068, \"component 1\": -0.2468964149055783, \"component 2\": 0.5587667860124729, \"label\": \"Asia\"}, {\"continent\": \"Europe\", \"country\": \"Poland\", \"component 0\": 0.1137133592001103, \"component 1\": -0.5857586276139185, \"component 2\": 1.2282640075958902, \"label\": \"Europe\"}, {\"continent\": \"Europe\", \"country\": \"Portugal\", \"component 0\": 1.3844655532695105, \"component 1\": -0.45247407141332746, \"component 2\": -1.7529969439179924, \"label\": \"Europe\"}, {\"continent\": \"Asia\", \"country\": \"Qatar\", \"component 0\": -2.581688197730867, \"component 1\": 0.7363708049471175, \"component 2\": -0.14577420703200106, \"label\": \"Asia\"}, {\"continent\": \"Europe\", \"country\": \"Romania\", \"component 0\": 0.25827830907700405, \"component 1\": -0.1778903725331505, \"component 2\": 0.2080623218294354, \"label\": \"Europe\"}, {\"continent\": \"Europe\", \"country\": \"Russia\", \"component 0\": -1.5975710877684557, \"component 1\": 0.7482393765483608, \"component 2\": 0.8398588569996296, \"label\": \"Europe\"}, {\"continent\": \"Asia\", \"country\": \"Saudi Arabia\", \"component 0\": -1.2169511229281023, \"component 1\": -0.34523765455731453, \"component 2\": -1.4084090472883277, \"label\": \"Asia\"}, {\"continent\": \"Asia\", \"country\": \"Singapore\", \"component 0\": -0.33198779523005734, \"component 1\": -0.9423859681500341, \"component 2\": -2.20752337400863, \"label\": \"Asia\"}, {\"continent\": \"Europe\", \"country\": \"Slovakia\", \"component 0\": 0.196213584967387, \"component 1\": 0.19712575555517808, \"component 2\": 1.614929739566174, \"label\": \"Europe\"}, {\"continent\": \"Europe\", \"country\": \"Slovenia\", \"component 0\": 0.7777098608044026, \"component 1\": 0.1412132685637221, \"component 2\": 1.0184547599843974, \"label\": \"Europe\"}, {\"continent\": \"Africa\", \"country\": \"South Africa\", \"component 0\": 0.5767047177260642, \"component 1\": -1.444178882057618, \"component 2\": 2.9287896894089216, \"label\": \"Africa\"}, {\"continent\": \"Asia\", \"country\": \"South Korea\", \"component 0\": 0.21255919897857414, \"component 1\": -0.8402899582816394, \"component 2\": 0.9040241202682462, \"label\": \"Asia\"}, {\"continent\": \"Europe\", \"country\": \"Spain\", \"component 0\": 1.2887741609981112, \"component 1\": -1.359170219689291, \"component 2\": -1.0402238383885443, \"label\": \"Europe\"}, {\"continent\": \"Asia\", \"country\": \"Sri Lanka\", \"component 0\": 0.7667849113239495, \"component 1\": -0.8498151503149699, \"component 2\": -0.9378616716015189, \"label\": \"Asia\"}, {\"continent\": \"Europe\", \"country\": \"Sweden\", \"component 0\": 2.7012133472869806, \"component 1\": 1.7632391683044457, \"component 2\": 0.6496430133001144, \"label\": \"Europe\"}, {\"continent\": \"Europe\", \"country\": \"Switzerland\", \"component 0\": 1.3627720955372418, \"component 1\": 0.6501185516707277, \"component 2\": 0.3609209070176543, \"label\": \"Europe\"}, {\"continent\": \"Asia\", \"country\": \"Taiwan\", \"component 0\": -0.0041978245073466145, \"component 1\": -0.8782300182476007, \"component 2\": 1.027947854688563, \"label\": \"Asia\"}, {\"continent\": \"Asia\", \"country\": \"Thailand\", \"component 0\": -0.4932957709392961, \"component 1\": -0.3635428818635353, \"component 2\": -0.4810303560431038, \"label\": \"Asia\"}, {\"continent\": \"North America\", \"country\": \"Trinidad and Tobago\", \"component 0\": -3.154195968923133, \"component 1\": 1.1571089913273216, \"component 2\": 0.3960492989914801, \"label\": \"North America\"}, {\"continent\": \"Asia\", \"country\": \"Turkey\", \"component 0\": 0.48907944384075, \"component 1\": -0.2427854376570046, \"component 2\": 0.5178495315387406, \"label\": \"Asia\"}, {\"continent\": \"Asia\", \"country\": \"Turkmenistan\", \"component 0\": -2.784632362862852, \"component 1\": 0.8777726862265139, \"component 2\": 0.041766802057585935, \"label\": \"Asia\"}, {\"continent\": \"Europe\", \"country\": \"Ukraine\", \"component 0\": -0.1381753195659778, \"component 1\": -0.19800889876221428, \"component 2\": 2.6915173157901466, \"label\": \"Europe\"}, {\"continent\": \"Asia\", \"country\": \"United Arab Emirates\", \"component 0\": -1.7351268299856142, \"component 1\": -0.10457038663939608, \"component 2\": -0.5342744711637402, \"label\": \"Asia\"}, {\"continent\": \"Europe\", \"country\": \"United Kingdom\", \"component 0\": 0.6423607687028606, \"component 1\": -0.32629836707262644, \"component 2\": -0.8481142361091327, \"label\": \"Europe\"}, {\"continent\": \"North America\", \"country\": \"United States\", \"component 0\": -0.14448373905480857, \"component 1\": -0.4650541756038492, \"component 2\": 0.04667262341161135, \"label\": \"North America\"}, {\"continent\": \"Asia\", \"country\": \"Uzbekistan\", \"component 0\": -3.056247028677703, \"component 1\": 1.3756770657885584, \"component 2\": 0.7986303616593537, \"label\": \"Asia\"}, {\"continent\": \"South America\", \"country\": \"Venezuela\", \"component 0\": -0.9960196887867551, \"component 1\": 1.6832972113795255, \"component 2\": -0.24758555952977288, \"label\": \"South America\"}, {\"continent\": \"Asia\", \"country\": \"Vietnam\", \"component 0\": 0.4390024796272064, \"component 1\": -0.38879424564501, \"component 2\": 1.9690284940800526, \"label\": \"Asia\"}], \"data-80a633a4265745509a95665888016bde\": [{\"variable\": \"coal\", \"component 0\": 0.9810473468195338, \"component 1\": -2.4448066930378376, \"component 2\": 2.1694947146335624, \"label\": \"coal\"}, {\"variable\": \"oil\", \"component 0\": 0.7508967431060225, \"component 1\": -2.5897317036096577, \"component 2\": -2.161022059579648, \"label\": \"oil\"}, {\"variable\": \"gas\", \"component 0\": -3.694517868249385, \"component 1\": 1.131235670220065, \"component 2\": -0.2836737246966873, \"label\": \"gas\"}, {\"variable\": \"nuclear\", \"component 0\": 1.322437831969654, \"component 1\": -0.016057665709922096, \"component 2\": 1.1732979457889494, \"label\": \"nuclear\"}, {\"variable\": \"hydro\", \"component 0\": 1.8767473363399203, \"component 1\": 4.604423444561435, \"component 2\": -0.04795815496062146, \"label\": \"hydro\"}, {\"variable\": \"wind\", \"component 0\": 2.2105568320108224, \"component 1\": -0.9984329958118455, \"component 2\": -1.067816629107604, \"label\": \"wind\"}, {\"variable\": \"solar\", \"component 0\": 1.7714605849411402, \"component 1\": -3.6429558552734256, \"component 2\": 0.0029629852657689273, \"label\": \"solar\"}, {\"variable\": \"other renewables\", \"component 0\": 2.679496725923172, \"component 1\": 3.1956762460643593, \"component 2\": -0.24635147510319505, \"label\": \"other renewables\"}]}}, {\"mode\": \"vega-lite\"});\n",
        "</script>"
       ],
       "text/plain": [
        "alt.LayerChart(...)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -906,7 +937,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 20,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-09-07T18:18:09.598983Z",
@@ -921,14 +952,14 @@
       "text/html": [
        "<style type=\"text/css\">\n",
        "</style>\n",
-       "<table id=\"T_76c2f\">\n",
+       "<table id=\"T_856b5\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank\" >&nbsp;</th>\n",
        "      <th class=\"index_name level0\" >component</th>\n",
-       "      <th id=\"T_76c2f_level0_col0\" class=\"col_heading level0 col0\" >0</th>\n",
-       "      <th id=\"T_76c2f_level0_col1\" class=\"col_heading level0 col1\" >1</th>\n",
-       "      <th id=\"T_76c2f_level0_col2\" class=\"col_heading level0 col2\" >2</th>\n",
+       "      <th id=\"T_856b5_level0_col0\" class=\"col_heading level0 col0\" >0</th>\n",
+       "      <th id=\"T_856b5_level0_col1\" class=\"col_heading level0 col1\" >1</th>\n",
+       "      <th id=\"T_856b5_level0_col2\" class=\"col_heading level0 col2\" >2</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th class=\"index_name level0\" >continent</th>\n",
@@ -940,54 +971,54 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_76c2f_level0_row0\" class=\"row_heading level0 row0\" >Africa</th>\n",
-       "      <th id=\"T_76c2f_level1_row0\" class=\"row_heading level1 row0\" >Algeria</th>\n",
-       "      <td id=\"T_76c2f_row0_col0\" class=\"data row0 col0\" >3%</td>\n",
-       "      <td id=\"T_76c2f_row0_col1\" class=\"data row0 col1\" >0%</td>\n",
-       "      <td id=\"T_76c2f_row0_col2\" class=\"data row0 col2\" >0%</td>\n",
+       "      <th id=\"T_856b5_level0_row0\" class=\"row_heading level0 row0\" >Africa</th>\n",
+       "      <th id=\"T_856b5_level1_row0\" class=\"row_heading level1 row0\" >Algeria</th>\n",
+       "      <td id=\"T_856b5_row0_col0\" class=\"data row0 col0\" >3%</td>\n",
+       "      <td id=\"T_856b5_row0_col1\" class=\"data row0 col1\" >0%</td>\n",
+       "      <td id=\"T_856b5_row0_col2\" class=\"data row0 col2\" >0%</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_76c2f_level0_row1\" class=\"row_heading level0 row1\" >South America</th>\n",
-       "      <th id=\"T_76c2f_level1_row1\" class=\"row_heading level1 row1\" >Argentina</th>\n",
-       "      <td id=\"T_76c2f_row1_col0\" class=\"data row1 col0\" >1%</td>\n",
-       "      <td id=\"T_76c2f_row1_col1\" class=\"data row1 col1\" >1%</td>\n",
-       "      <td id=\"T_76c2f_row1_col2\" class=\"data row1 col2\" >0%</td>\n",
+       "      <th id=\"T_856b5_level0_row1\" class=\"row_heading level0 row1\" >South America</th>\n",
+       "      <th id=\"T_856b5_level1_row1\" class=\"row_heading level1 row1\" >Argentina</th>\n",
+       "      <td id=\"T_856b5_row1_col0\" class=\"data row1 col0\" >1%</td>\n",
+       "      <td id=\"T_856b5_row1_col1\" class=\"data row1 col1\" >1%</td>\n",
+       "      <td id=\"T_856b5_row1_col2\" class=\"data row1 col2\" >0%</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_76c2f_level0_row2\" class=\"row_heading level0 row2\" >Oceania</th>\n",
-       "      <th id=\"T_76c2f_level1_row2\" class=\"row_heading level1 row2\" >Australia</th>\n",
-       "      <td id=\"T_76c2f_row2_col0\" class=\"data row2 col0\" >0%</td>\n",
-       "      <td id=\"T_76c2f_row2_col1\" class=\"data row2 col1\" >2%</td>\n",
-       "      <td id=\"T_76c2f_row2_col2\" class=\"data row2 col2\" >0%</td>\n",
+       "      <th id=\"T_856b5_level0_row2\" class=\"row_heading level0 row2\" >Oceania</th>\n",
+       "      <th id=\"T_856b5_level1_row2\" class=\"row_heading level1 row2\" >Australia</th>\n",
+       "      <td id=\"T_856b5_row2_col0\" class=\"data row2 col0\" >0%</td>\n",
+       "      <td id=\"T_856b5_row2_col1\" class=\"data row2 col1\" >2%</td>\n",
+       "      <td id=\"T_856b5_row2_col2\" class=\"data row2 col2\" >0%</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_76c2f_level0_row3\" class=\"row_heading level0 row3\" >Europe</th>\n",
-       "      <th id=\"T_76c2f_level1_row3\" class=\"row_heading level1 row3\" >Austria</th>\n",
-       "      <td id=\"T_76c2f_row3_col0\" class=\"data row3 col0\" >0%</td>\n",
-       "      <td id=\"T_76c2f_row3_col1\" class=\"data row3 col1\" >1%</td>\n",
-       "      <td id=\"T_76c2f_row3_col2\" class=\"data row3 col2\" >0%</td>\n",
+       "      <th id=\"T_856b5_level0_row3\" class=\"row_heading level0 row3\" >Europe</th>\n",
+       "      <th id=\"T_856b5_level1_row3\" class=\"row_heading level1 row3\" >Austria</th>\n",
+       "      <td id=\"T_856b5_row3_col0\" class=\"data row3 col0\" >0%</td>\n",
+       "      <td id=\"T_856b5_row3_col1\" class=\"data row3 col1\" >1%</td>\n",
+       "      <td id=\"T_856b5_row3_col2\" class=\"data row3 col2\" >0%</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_76c2f_level0_row4\" class=\"row_heading level0 row4\" >Asia</th>\n",
-       "      <th id=\"T_76c2f_level1_row4\" class=\"row_heading level1 row4\" >Azerbaijan</th>\n",
-       "      <td id=\"T_76c2f_row4_col0\" class=\"data row4 col0\" >3%</td>\n",
-       "      <td id=\"T_76c2f_row4_col1\" class=\"data row4 col1\" >0%</td>\n",
-       "      <td id=\"T_76c2f_row4_col2\" class=\"data row4 col2\" >0%</td>\n",
+       "      <th id=\"T_856b5_level0_row4\" class=\"row_heading level0 row4\" >Asia</th>\n",
+       "      <th id=\"T_856b5_level1_row4\" class=\"row_heading level1 row4\" >Azerbaijan</th>\n",
+       "      <td id=\"T_856b5_row4_col0\" class=\"data row4 col0\" >3%</td>\n",
+       "      <td id=\"T_856b5_row4_col1\" class=\"data row4 col1\" >0%</td>\n",
+       "      <td id=\"T_856b5_row4_col2\" class=\"data row4 col2\" >0%</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x16d643f50>"
+       "<pandas.io.formats.style.Styler at 0x12eec60d0>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pca.row_contributions_.head().style.format('{:.0%}')  "
+    "pca.row_contributions_.head().style.format('{:.0%}')"
    ]
   },
   {
@@ -1008,7 +1039,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 21,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-09-07T18:18:09.611924Z",
@@ -1023,13 +1054,13 @@
       "text/html": [
        "<style type=\"text/css\">\n",
        "</style>\n",
-       "<table id=\"T_e32c8\">\n",
+       "<table id=\"T_a63df\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"index_name level0\" >component</th>\n",
-       "      <th id=\"T_e32c8_level0_col0\" class=\"col_heading level0 col0\" >0</th>\n",
-       "      <th id=\"T_e32c8_level0_col1\" class=\"col_heading level0 col1\" >1</th>\n",
-       "      <th id=\"T_e32c8_level0_col2\" class=\"col_heading level0 col2\" >2</th>\n",
+       "      <th id=\"T_a63df_level0_col0\" class=\"col_heading level0 col0\" >0</th>\n",
+       "      <th id=\"T_a63df_level0_col1\" class=\"col_heading level0 col1\" >1</th>\n",
+       "      <th id=\"T_a63df_level0_col2\" class=\"col_heading level0 col2\" >2</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th class=\"index_name level0\" >variable</th>\n",
@@ -1040,61 +1071,61 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_e32c8_level0_row0\" class=\"row_heading level0 row0\" >coal</th>\n",
-       "      <td id=\"T_e32c8_row0_col0\" class=\"data row0 col0\" >3%</td>\n",
-       "      <td id=\"T_e32c8_row0_col1\" class=\"data row0 col1\" >10%</td>\n",
-       "      <td id=\"T_e32c8_row0_col2\" class=\"data row0 col2\" >39%</td>\n",
+       "      <th id=\"T_a63df_level0_row0\" class=\"row_heading level0 row0\" >coal</th>\n",
+       "      <td id=\"T_a63df_row0_col0\" class=\"data row0 col0\" >3%</td>\n",
+       "      <td id=\"T_a63df_row0_col1\" class=\"data row0 col1\" >10%</td>\n",
+       "      <td id=\"T_a63df_row0_col2\" class=\"data row0 col2\" >39%</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e32c8_level0_row1\" class=\"row_heading level0 row1\" >oil</th>\n",
-       "      <td id=\"T_e32c8_row1_col0\" class=\"data row1 col0\" >2%</td>\n",
-       "      <td id=\"T_e32c8_row1_col1\" class=\"data row1 col1\" >11%</td>\n",
-       "      <td id=\"T_e32c8_row1_col2\" class=\"data row1 col2\" >39%</td>\n",
+       "      <th id=\"T_a63df_level0_row1\" class=\"row_heading level0 row1\" >oil</th>\n",
+       "      <td id=\"T_a63df_row1_col0\" class=\"data row1 col0\" >2%</td>\n",
+       "      <td id=\"T_a63df_row1_col1\" class=\"data row1 col1\" >11%</td>\n",
+       "      <td id=\"T_a63df_row1_col2\" class=\"data row1 col2\" >39%</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e32c8_level0_row2\" class=\"row_heading level0 row2\" >gas</th>\n",
-       "      <td id=\"T_e32c8_row2_col0\" class=\"data row2 col0\" >38%</td>\n",
-       "      <td id=\"T_e32c8_row2_col1\" class=\"data row2 col1\" >2%</td>\n",
-       "      <td id=\"T_e32c8_row2_col2\" class=\"data row2 col2\" >1%</td>\n",
+       "      <th id=\"T_a63df_level0_row2\" class=\"row_heading level0 row2\" >gas</th>\n",
+       "      <td id=\"T_a63df_row2_col0\" class=\"data row2 col0\" >38%</td>\n",
+       "      <td id=\"T_a63df_row2_col1\" class=\"data row2 col1\" >2%</td>\n",
+       "      <td id=\"T_a63df_row2_col2\" class=\"data row2 col2\" >1%</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e32c8_level0_row3\" class=\"row_heading level0 row3\" >nuclear</th>\n",
-       "      <td id=\"T_e32c8_row3_col0\" class=\"data row3 col0\" >5%</td>\n",
-       "      <td id=\"T_e32c8_row3_col1\" class=\"data row3 col1\" >0%</td>\n",
-       "      <td id=\"T_e32c8_row3_col2\" class=\"data row3 col2\" >11%</td>\n",
+       "      <th id=\"T_a63df_level0_row3\" class=\"row_heading level0 row3\" >nuclear</th>\n",
+       "      <td id=\"T_a63df_row3_col0\" class=\"data row3 col0\" >5%</td>\n",
+       "      <td id=\"T_a63df_row3_col1\" class=\"data row3 col1\" >0%</td>\n",
+       "      <td id=\"T_a63df_row3_col2\" class=\"data row3 col2\" >11%</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e32c8_level0_row4\" class=\"row_heading level0 row4\" >hydro</th>\n",
-       "      <td id=\"T_e32c8_row4_col0\" class=\"data row4 col0\" >10%</td>\n",
-       "      <td id=\"T_e32c8_row4_col1\" class=\"data row4 col1\" >36%</td>\n",
-       "      <td id=\"T_e32c8_row4_col2\" class=\"data row4 col2\" >0%</td>\n",
+       "      <th id=\"T_a63df_level0_row4\" class=\"row_heading level0 row4\" >hydro</th>\n",
+       "      <td id=\"T_a63df_row4_col0\" class=\"data row4 col0\" >10%</td>\n",
+       "      <td id=\"T_a63df_row4_col1\" class=\"data row4 col1\" >36%</td>\n",
+       "      <td id=\"T_a63df_row4_col2\" class=\"data row4 col2\" >0%</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e32c8_level0_row5\" class=\"row_heading level0 row5\" >wind</th>\n",
-       "      <td id=\"T_e32c8_row5_col0\" class=\"data row5 col0\" >14%</td>\n",
-       "      <td id=\"T_e32c8_row5_col1\" class=\"data row5 col1\" >2%</td>\n",
-       "      <td id=\"T_e32c8_row5_col2\" class=\"data row5 col2\" >9%</td>\n",
+       "      <th id=\"T_a63df_level0_row5\" class=\"row_heading level0 row5\" >wind</th>\n",
+       "      <td id=\"T_a63df_row5_col0\" class=\"data row5 col0\" >14%</td>\n",
+       "      <td id=\"T_a63df_row5_col1\" class=\"data row5 col1\" >2%</td>\n",
+       "      <td id=\"T_a63df_row5_col2\" class=\"data row5 col2\" >9%</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e32c8_level0_row6\" class=\"row_heading level0 row6\" >solar</th>\n",
-       "      <td id=\"T_e32c8_row6_col0\" class=\"data row6 col0\" >9%</td>\n",
-       "      <td id=\"T_e32c8_row6_col1\" class=\"data row6 col1\" >22%</td>\n",
-       "      <td id=\"T_e32c8_row6_col2\" class=\"data row6 col2\" >0%</td>\n",
+       "      <th id=\"T_a63df_level0_row6\" class=\"row_heading level0 row6\" >solar</th>\n",
+       "      <td id=\"T_a63df_row6_col0\" class=\"data row6 col0\" >9%</td>\n",
+       "      <td id=\"T_a63df_row6_col1\" class=\"data row6 col1\" >22%</td>\n",
+       "      <td id=\"T_a63df_row6_col2\" class=\"data row6 col2\" >0%</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_e32c8_level0_row7\" class=\"row_heading level0 row7\" >other renewables</th>\n",
-       "      <td id=\"T_e32c8_row7_col0\" class=\"data row7 col0\" >20%</td>\n",
-       "      <td id=\"T_e32c8_row7_col1\" class=\"data row7 col1\" >17%</td>\n",
-       "      <td id=\"T_e32c8_row7_col2\" class=\"data row7 col2\" >1%</td>\n",
+       "      <th id=\"T_a63df_level0_row7\" class=\"row_heading level0 row7\" >other renewables</th>\n",
+       "      <td id=\"T_a63df_row7_col0\" class=\"data row7 col0\" >20%</td>\n",
+       "      <td id=\"T_a63df_row7_col1\" class=\"data row7 col1\" >17%</td>\n",
+       "      <td id=\"T_a63df_row7_col2\" class=\"data row7 col2\" >1%</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x16d49db90>"
+       "<pandas.io.formats.style.Styler at 0x12eec5e50>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1113,7 +1144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 22,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-09-07T18:18:09.624524Z",
@@ -1207,7 +1238,7 @@
        "Asia          Azerbaijan  0.868801  0.072377  0.024190"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1218,7 +1249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 23,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-09-07T18:18:09.637215Z",
@@ -1326,7 +1357,7 @@
        "other renewables  0.395327  0.267222  0.007075"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1345,7 +1376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 24,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-09-07T18:18:09.650202Z",
@@ -1453,7 +1484,7 @@
        "other renewables  0.628750  0.516935 -0.084114"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1464,7 +1495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 25,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-09-07T18:18:09.663703Z",
@@ -1480,7 +1511,7 @@
        "True"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1507,7 +1538,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 26,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-09-07T18:18:09.675742Z",
@@ -1627,9 +1658,9 @@
        "</div>"
       ],
       "text/plain": [
-       "                                 0         1         2         3         4   \n",
+       "                                 0         1         2         3         4  \\\n",
        "continent     country                                                        \n",
-       "Africa        Algeria     0.024204  0.369866  0.599244 -0.007082  0.024826  \\\n",
+       "Africa        Algeria     0.024204  0.369866  0.599244 -0.007082  0.024826   \n",
        "South America Argentina   0.027773  0.366342  0.487562  0.008596  0.090082   \n",
        "Oceania       Australia   0.287372  0.425348  0.208594  0.056641 -0.018484   \n",
        "Europe        Austria     0.060875  0.410393  0.219553  0.040122  0.183231   \n",
@@ -1644,7 +1675,7 @@
        "Asia          Azerbaijan -0.004640 -0.000439 -0.005092  "
       ]
      },
-     "execution_count": 18,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1664,7 +1695,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 27,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-09-07T18:18:09.689286Z",
@@ -1677,10 +1708,10 @@
     {
      "data": {
       "text/plain": [
-       "1.1927159589872418"
+       "np.float64(1.1927159589872411)"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1711,7 +1742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 28,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-09-07T18:18:09.700856Z",
@@ -1736,7 +1767,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 29,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-09-07T18:18:09.722890Z",
@@ -1813,7 +1844,7 @@
        "              United States       -0.226122 -0.433260"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1832,7 +1863,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 30,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-09-07T18:18:09.756636Z",
@@ -1900,18 +1931,18 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>hydro</th>\n",
-       "      <td>0.246646</td>\n",
-       "      <td>0.037118</td>\n",
+       "      <td>2.192240</td>\n",
+       "      <td>0.329911</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>wind</th>\n",
-       "      <td>0.195675</td>\n",
-       "      <td>0.184247</td>\n",
+       "      <td>1.739201</td>\n",
+       "      <td>1.637625</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>solar</th>\n",
-       "      <td>0.251247</td>\n",
-       "      <td>0.076237</td>\n",
+       "      <td>2.233130</td>\n",
+       "      <td>0.677610</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1925,12 +1956,12 @@
        "gas              -0.916078 -0.331546\n",
        "nuclear           0.384458 -0.401675\n",
        "other renewables  0.467116  0.086656\n",
-       "hydro             0.246646  0.037118\n",
-       "wind              0.195675  0.184247\n",
-       "solar             0.251247  0.076237"
+       "hydro             2.192240  0.329911\n",
+       "wind              1.739201  1.637625\n",
+       "solar             2.233130  0.677610"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1950,7 +1981,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 31,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-09-07T18:18:09.794815Z",
@@ -1964,6 +1995,117 @@
     "pca = prince.PCA().fit(active, supplementary_columns=['hydro', 'wind', 'solar'])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Weighting\n",
+    "\n",
+    "Rows and columns can be weighted differently when fitting the PCA."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "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>component</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>continent</th>\n",
+       "      <th>country</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Africa</th>\n",
+       "      <th>Algeria</th>\n",
+       "      <td>-1.512601</td>\n",
+       "      <td>-1.806884</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>South America</th>\n",
+       "      <th>Argentina</th>\n",
+       "      <td>-0.403372</td>\n",
+       "      <td>-1.475517</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Oceania</th>\n",
+       "      <th>Australia</th>\n",
+       "      <td>-1.441339</td>\n",
+       "      <td>1.394480</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Europe</th>\n",
+       "      <th>Austria</th>\n",
+       "      <td>1.521697</td>\n",
+       "      <td>0.040025</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Asia</th>\n",
+       "      <th>Azerbaijan</th>\n",
+       "      <td>-1.272744</td>\n",
+       "      <td>-1.980454</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "component                        0         1\n",
+       "continent     country                       \n",
+       "Africa        Algeria    -1.512601 -1.806884\n",
+       "South America Argentina  -0.403372 -1.475517\n",
+       "Oceania       Australia  -1.441339  1.394480\n",
+       "Europe        Austria     1.521697  0.040025\n",
+       "Asia          Azerbaijan -1.272744 -1.980454"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "row_weights = np.ones(active.shape[0])\n",
+    "row_weights[active.index.get_level_values('continent') == 'Europe'] = 2\n",
+    "\n",
+    "column_weights = np.ones(active.shape[1])\n",
+    "column_weights[active.columns.isin(['hydro', 'wind', 'solar', 'other renewables'])] = 2\n",
+    "\n",
+    "pca = prince.PCA().fit(\n",
+    "    active,\n",
+    "    sample_weight=row_weights,\n",
+    "    column_weight=column_weights\n",
+    ")\n",
+    "pca.transform(dataset).head()"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -2318,7 +2460,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "prince-NQ1O93Uh-py3.13",
    "language": "python",
    "name": "python3"
   },
@@ -2332,12 +2474,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "441c2ec70d9faeb70e7723f55150c6260f4a26a9c828b90915d3399002e14f43"
-   }
+   "version": "3.13.0"
   }
  },
  "nbformat": 4,
diff --git a/poetry.lock b/poetry.lock
index 02c2c3d4..b752829b 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,27 +1,28 @@
-# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
 
 [[package]]
 name = "altair"
-version = "5.4.1"
+version = "5.5.0"
 description = "Vega-Altair: A declarative statistical visualization library for Python."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "altair-5.4.1-py3-none-any.whl", hash = "sha256:0fb130b8297a569d08991fb6fe763582e7569f8a04643bbd9212436e3be04aef"},
-    {file = "altair-5.4.1.tar.gz", hash = "sha256:0ce8c2e66546cb327e5f2d7572ec0e7c6feece816203215613962f0ec1d76a82"},
+    {file = "altair-5.5.0-py3-none-any.whl", hash = "sha256:91a310b926508d560fe0148d02a194f38b824122641ef528113d029fcd129f8c"},
+    {file = "altair-5.5.0.tar.gz", hash = "sha256:d960ebe6178c56de3855a68c47b516be38640b73fb3b5111c2a9ca90546dd73d"},
 ]
 
 [package.dependencies]
 jinja2 = "*"
 jsonschema = ">=3.0"
-narwhals = ">=1.5.2"
+narwhals = ">=1.14.2"
 packaging = "*"
-typing-extensions = {version = ">=4.10.0", markers = "python_version < \"3.13\""}
+typing-extensions = {version = ">=4.10.0", markers = "python_version < \"3.14\""}
 
 [package.extras]
-all = ["altair-tiles (>=0.3.0)", "anywidget (>=0.9.0)", "numpy", "pandas (>=0.25.3)", "pyarrow (>=11)", "vega-datasets (>=0.9.0)", "vegafusion[embed] (>=1.6.6)", "vl-convert-python (>=1.6.0)"]
-dev = ["geopandas", "hatch", "ibis-framework[polars]", "ipython[kernel]", "mistune", "mypy", "pandas (>=0.25.3)", "pandas-stubs", "polars (>=0.20.3)", "pytest", "pytest-cov", "pytest-xdist[psutil] (>=3.5,<4.0)", "ruff (>=0.6.0)", "types-jsonschema", "types-setuptools"]
+all = ["altair-tiles (>=0.3.0)", "anywidget (>=0.9.0)", "numpy", "pandas (>=1.1.3)", "pyarrow (>=11)", "vega-datasets (>=0.9.0)", "vegafusion[embed] (>=1.6.6)", "vl-convert-python (>=1.7.0)"]
+dev = ["duckdb (>=1.0)", "geopandas", "hatch (>=1.13.0)", "ipython[kernel]", "mistune", "mypy", "pandas (>=1.1.3)", "pandas-stubs", "polars (>=0.20.3)", "pyarrow-stubs", "pytest", "pytest-cov", "pytest-xdist[psutil] (>=3.5,<4.0)", "ruff (>=0.6.0)", "types-jsonschema", "types-setuptools"]
 doc = ["docutils", "jinja2", "myst-parser", "numpydoc", "pillow (>=9,<10)", "pydata-sphinx-theme (>=0.14.1)", "scipy", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinxext-altair"]
+save = ["vl-convert-python (>=1.7.0)"]
 
 [[package]]
 name = "appnope"
@@ -36,37 +37,34 @@ files = [
 
 [[package]]
 name = "asttokens"
-version = "2.4.1"
+version = "3.0.0"
 description = "Annotate AST trees with source code positions"
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
 files = [
-    {file = "asttokens-2.4.1-py2.py3-none-any.whl", hash = "sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24"},
-    {file = "asttokens-2.4.1.tar.gz", hash = "sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0"},
+    {file = "asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2"},
+    {file = "asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7"},
 ]
 
-[package.dependencies]
-six = ">=1.12.0"
-
 [package.extras]
-astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
-test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
+astroid = ["astroid (>=2,<4)"]
+test = ["astroid (>=2,<4)", "pytest", "pytest-cov", "pytest-xdist"]
 
 [[package]]
 name = "attrs"
-version = "24.2.0"
+version = "24.3.0"
 description = "Classes Without Boilerplate"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "attrs-24.2.0-py3-none-any.whl", hash = "sha256:81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2"},
-    {file = "attrs-24.2.0.tar.gz", hash = "sha256:5cfb1b9148b5b086569baec03f20d7b6bf3bcacc9a42bebf87ffaaca362f6346"},
+    {file = "attrs-24.3.0-py3-none-any.whl", hash = "sha256:ac96cd038792094f438ad1f6ff80837353805ac950cd2aa0e0625ef19850c308"},
+    {file = "attrs-24.3.0.tar.gz", hash = "sha256:8f5c07333d543103541ba7be0e2ce16eeee8130cb0b3f9238ab904ce1e85baff"},
 ]
 
 [package.extras]
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+dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit-uv", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
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 tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
@@ -92,41 +90,6 @@ charset-normalizer = ["charset-normalizer"]
 html5lib = ["html5lib"]
 lxml = ["lxml"]
 
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-
 [[package]]
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 version = "6.2.0"
@@ -139,6 +102,7 @@ files = [
 ]
 
 [package.dependencies]
+tinycss2 = {version = ">=1.1.0,<1.5", optional = true, markers = "extra == \"css\""}
 webencodings = "*"
 
 [package.extras]
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-
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -278,37 +228,37 @@ test = ["pytest"]
 
 [[package]]
 name = "debugpy"
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 ]
 
 [[package]]
@@ -374,13 +324,13 @@ tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipyth
 
 [[package]]
 name = "fastjsonschema"
-version = "2.20.0"
+version = "2.21.1"
 description = "Fastest Python implementation of JSON schema"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.extras]
@@ -414,41 +364,18 @@ typing = ["typing-extensions (>=4.12.2)"]
 
 [[package]]
 name = "identify"
-version = "2.6.2"
+version = "2.6.4"
 description = "File identification library for Python"
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
 [package.extras]
 license = ["ukkonen"]
 
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-version = "8.5.0"
-description = "Read metadata from Python packages"
-optional = false
-python-versions = ">=3.8"
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-
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-cover = ["pytest-cov"]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-enabler = ["pytest-enabler (>=2.2)"]
-perf = ["ipython"]
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-type = ["pytest-mypy"]
-
 [[package]]
 name = "iniconfig"
 version = "2.0.0"
@@ -495,13 +422,13 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio
 
 [[package]]
 name = "ipython"
-version = "8.18.1"
+version = "8.31.0"
 description = "IPython: Productive Interactive Computing"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
 files = [
-    {file = "ipython-8.18.1-py3-none-any.whl", hash = "sha256:e8267419d72d81955ec1177f8a29aaa90ac80ad647499201119e2f05e99aa397"},
-    {file = "ipython-8.18.1.tar.gz", hash = "sha256:ca6f079bb33457c66e233e4580ebfc4128855b4cf6370dddd73842a9563e8a27"},
+    {file = "ipython-8.31.0-py3-none-any.whl", hash = "sha256:46ec58f8d3d076a61d128fe517a51eb730e3aaf0c184ea8c17d16e366660c6a6"},
+    {file = "ipython-8.31.0.tar.gz", hash = "sha256:b6a2274606bec6166405ff05e54932ed6e5cfecaca1fc05f2cacde7bb074d70b"},
 ]
 
 [package.dependencies]
@@ -510,25 +437,26 @@ decorator = "*"
 exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
 jedi = ">=0.16"
 matplotlib-inline = "*"
-pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
-prompt-toolkit = ">=3.0.41,<3.1.0"
+pexpect = {version = ">4.3", markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\""}
+prompt_toolkit = ">=3.0.41,<3.1.0"
 pygments = ">=2.4.0"
-stack-data = "*"
-traitlets = ">=5"
-typing-extensions = {version = "*", markers = "python_version < \"3.10\""}
+stack_data = "*"
+traitlets = ">=5.13.0"
+typing_extensions = {version = ">=4.6", markers = "python_version < \"3.12\""}
 
 [package.extras]
-all = ["black", "curio", "docrepr", "exceptiongroup", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio (<0.22)", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
+all = ["ipython[black,doc,kernel,matplotlib,nbconvert,nbformat,notebook,parallel,qtconsole]", "ipython[test,test-extra]"]
 black = ["black"]
-doc = ["docrepr", "exceptiongroup", "ipykernel", "matplotlib", "pickleshare", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio (<0.22)", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
+doc = ["docrepr", "exceptiongroup", "intersphinx_registry", "ipykernel", "ipython[test]", "matplotlib", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "sphinxcontrib-jquery", "tomli", "typing_extensions"]
 kernel = ["ipykernel"]
+matplotlib = ["matplotlib"]
 nbconvert = ["nbconvert"]
 nbformat = ["nbformat"]
 notebook = ["ipywidgets", "notebook"]
 parallel = ["ipyparallel"]
 qtconsole = ["qtconsole"]
-test = ["pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath"]
-test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath", "trio"]
+test = ["packaging", "pickleshare", "pytest", "pytest-asyncio (<0.22)", "testpath"]
+test-extra = ["curio", "ipython[test]", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.23)", "pandas", "trio"]
 
 [[package]]
 name = "jedi"
@@ -624,7 +552,6 @@ files = [
 ]
 
 [package.dependencies]
-importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
 jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
 python-dateutil = ">=2.8.2"
 pyzmq = ">=23.0"
@@ -752,54 +679,50 @@ traitlets = "*"
 
 [[package]]
 name = "mistune"
-version = "3.0.2"
+version = "3.1.0"
 description = "A sane and fast Markdown parser with useful plugins and renderers"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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+    {file = "mistune-3.1.0-py3-none-any.whl", hash = "sha256:b05198cf6d671b3deba6c87ec6cf0d4eb7b72c524636eddb6dbf13823b52cee1"},
+    {file = "mistune-3.1.0.tar.gz", hash = "sha256:dbcac2f78292b9dc066cd03b7a3a26b62d85f8159f2ea5fd28e55df79908d667"},
 ]
 
-[[package]]
-name = "mypy-extensions"
-version = "1.0.0"
-description = "Type system extensions for programs checked with the mypy type checker."
-optional = false
-python-versions = ">=3.5"
-files = [
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-]
+[package.dependencies]
+typing-extensions = {version = "*", markers = "python_version < \"3.11\""}
 
 [[package]]
 name = "narwhals"
-version = "1.13.5"
+version = "1.20.1"
 description = "Extremely lightweight compatibility layer between dataframe libraries"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
-cudf = ["cudf (>=23.08.00)"]
+cudf = ["cudf (>=24.10.0)"]
 dask = ["dask[dataframe] (>=2024.7)"]
+dev = ["covdefaults", "duckdb", "hypothesis[numpy]", "pandas", "polars", "pre-commit", "pyarrow", "pyarrow-stubs", "pytest", "pytest-cov", "pytest-env", "pytest-randomly", "tqdm", "typing-extensions"]
+docs = ["black", "duckdb", "jinja2", "markdown-exec[ansi]", "mkdocs", "mkdocs-autorefs", "mkdocs-material", "mkdocstrings[python]", "pandas", "polars (>=1.0.0)", "pyarrow"]
+extra = ["dask[dataframe]", "modin", "pyspark", "scikit-learn"]
 modin = ["modin"]
 pandas = ["pandas (>=0.25.3)"]
 polars = ["polars (>=0.20.3)"]
 pyarrow = ["pyarrow (>=11.0.0)"]
+pyspark = ["pyspark (>=3.3.0)"]
 
 [[package]]
 name = "nbclient"
-version = "0.10.0"
+version = "0.10.2"
 description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
 optional = false
-python-versions = ">=3.8.0"
+python-versions = ">=3.9.0"
 files = [
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-    {file = "nbclient-0.10.0.tar.gz", hash = "sha256:4b3f1b7dba531e498449c4db4f53da339c91d449dc11e9af3a43b4eb5c5abb09"},
+    {file = "nbclient-0.10.2-py3-none-any.whl", hash = "sha256:4ffee11e788b4a27fabeb7955547e4318a5298f34342a4bfd01f2e1faaeadc3d"},
+    {file = "nbclient-0.10.2.tar.gz", hash = "sha256:90b7fc6b810630db87a6d0c2250b1f0ab4cf4d3c27a299b0cde78a4ed3fd9193"},
 ]
 
 [package.dependencies]
@@ -810,25 +733,24 @@ traitlets = ">=5.4"
 
 [package.extras]
 dev = ["pre-commit"]
-docs = ["autodoc-traits", "mock", "moto", "myst-parser", "nbclient[test]", "sphinx (>=1.7)", "sphinx-book-theme", "sphinxcontrib-spelling"]
-test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "pytest (>=7.0,<8)", "pytest-asyncio", "pytest-cov (>=4.0)", "testpath", "xmltodict"]
+docs = ["autodoc-traits", "flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "mock", "moto", "myst-parser", "nbconvert (>=7.1.0)", "pytest (>=7.0,<8)", "pytest-asyncio", "pytest-cov (>=4.0)", "sphinx (>=1.7)", "sphinx-book-theme", "sphinxcontrib-spelling", "testpath", "xmltodict"]
+test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=7.1.0)", "pytest (>=7.0,<8)", "pytest-asyncio", "pytest-cov (>=4.0)", "testpath", "xmltodict"]
 
 [[package]]
 name = "nbconvert"
-version = "7.16.4"
+version = "7.16.5"
 description = "Converting Jupyter Notebooks (.ipynb files) to other formats.  Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script.  nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "nbconvert-7.16.5-py3-none-any.whl", hash = "sha256:e12eac052d6fd03040af4166c563d76e7aeead2e9aadf5356db552a1784bd547"},
+    {file = "nbconvert-7.16.5.tar.gz", hash = "sha256:c83467bb5777fdfaac5ebbb8e864f300b277f68692ecc04d6dab72f2d8442344"},
 ]
 
 [package.dependencies]
 beautifulsoup4 = "*"
-bleach = "!=5.0.0"
+bleach = {version = "!=5.0.0", extras = ["css"]}
 defusedxml = "*"
-importlib-metadata = {version = ">=3.6", markers = "python_version < \"3.10\""}
 jinja2 = ">=3.0"
 jupyter-core = ">=4.7"
 jupyterlab-pygments = "*"
@@ -839,7 +761,6 @@ nbformat = ">=5.7"
 packaging = "*"
 pandocfilters = ">=1.4.1"
 pygments = ">=2.4.1"
-tinycss2 = "*"
 traitlets = ">=5.1"
 
 [package.extras]
@@ -896,56 +817,66 @@ files = [
 
 [[package]]
 name = "numpy"
-version = "2.0.2"
+version = "2.2.1"
 description = "Fundamental package for array computing in Python"
 optional = false
-python-versions = ">=3.9"
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 [package.extras]
-dev = ["black", "check-manifest", "coverage", "packaging", "pylint", "pyperf", "pypinfo", "pytest-cov", "requests", "rstcheck", "ruff", "sphinx", "sphinx_rtd_theme", "toml-sort", "twine", "virtualenv", "wheel"]
+dev = ["abi3audit", "black", "check-manifest", "coverage", "packaging", "pylint", "pyperf", "pypinfo", "pytest-cov", "requests", "rstcheck", "ruff", "sphinx", "sphinx_rtd_theme", "toml-sort", "twine", "virtualenv", "vulture", "wheel"]
 test = ["pytest", "pytest-xdist", "setuptools"]
 
 [[package]]
@@ -1241,13 +1161,13 @@ windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "pytest"
-version = "7.4.4"
+version = "8.3.4"
 description = "pytest: simple powerful testing with Python"
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-python-versions = ">=3.7"
+python-versions = ">=3.8"
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 iniconfig = "*"
 packaging = "*"
-pluggy = ">=0.12,<2.0"
-tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
+pluggy = ">=1.5,<2"
+tomli = {version = ">=1", markers = "python_version < \"3.11\""}
 
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-testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
+dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
 [[package]]
 name = "python-dateutil"
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 tzlocal = "*"
 
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 [[package]]
@@ -1827,33 +1778,63 @@ test = ["pytest", "ruff"]
 
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+    {file = "tornado-6.4.2-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bca9eb02196e789c9cb5c3c7c0f04fb447dc2adffd95265b2c7223a8a615ccbf"},
+    {file = "tornado-6.4.2-cp38-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:304463bd0772442ff4d0f5149c6f1c2135a1fae045adf070821c6cdc76980634"},
+    {file = "tornado-6.4.2-cp38-abi3-musllinux_1_2_i686.whl", hash = "sha256:c82c46813ba483a385ab2a99caeaedf92585a1f90defb5693351fa7e4ea0bf73"},
+    {file = "tornado-6.4.2-cp38-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:932d195ca9015956fa502c6b56af9eb06106140d844a335590c1ec7f5277d10c"},
+    {file = "tornado-6.4.2-cp38-abi3-win32.whl", hash = "sha256:2876cef82e6c5978fde1e0d5b1f919d756968d5b4282418f3146b79b58556482"},
+    {file = "tornado-6.4.2-cp38-abi3-win_amd64.whl", hash = "sha256:908b71bf3ff37d81073356a5fadcc660eb10c1476ee6e2725588626ce7e5ca38"},
+    {file = "tornado-6.4.2.tar.gz", hash = "sha256:92bad5b4746e9879fd7bf1eb21dce4e3fc5128d71601f80005afa39237ad620b"},
 ]
 
 [[package]]
@@ -1912,13 +1893,13 @@ devenv = ["check-manifest", "pytest (>=4.3)", "pytest-cov", "pytest-mock (>=3.3)
 
 [[package]]
 name = "virtualenv"
-version = "20.27.1"
+version = "20.28.1"
 description = "Virtual Python Environment builder"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "virtualenv-20.27.1-py3-none-any.whl", hash = "sha256:f11f1b8a29525562925f745563bfd48b189450f61fb34c4f9cc79dd5aa32a1f4"},
-    {file = "virtualenv-20.27.1.tar.gz", hash = "sha256:142c6be10212543b32c6c45d3d3893dff89112cc588b7d0879ae5a1ec03a47ba"},
+    {file = "virtualenv-20.28.1-py3-none-any.whl", hash = "sha256:412773c85d4dab0409b83ec36f7a6499e72eaf08c80e81e9576bca61831c71cb"},
+    {file = "virtualenv-20.28.1.tar.gz", hash = "sha256:5d34ab240fdb5d21549b76f9e8ff3af28252f5499fb6d6f031adac4e5a8c5329"},
 ]
 
 [package.dependencies]
@@ -1954,47 +1935,30 @@ files = [
 
 [[package]]
 name = "xarray"
-version = "2023.12.0"
+version = "2025.1.0"
 description = "N-D labeled arrays and datasets in Python"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
 files = [
-    {file = "xarray-2023.12.0-py3-none-any.whl", hash = "sha256:3c22b6824681762b6c3fcad86dfd18960a617bccbc7f456ce21b43a20e455fb9"},
-    {file = "xarray-2023.12.0.tar.gz", hash = "sha256:4565dbc890de47e278346c44d6b33bb07d3427383e077a7ca8ab6606196fd433"},
+    {file = "xarray-2025.1.0-py3-none-any.whl", hash = "sha256:bff8017a47f6092c152c4745307a26adc3e31bfeb1b81abe6a2e62eda99a3687"},
+    {file = "xarray-2025.1.0.tar.gz", hash = "sha256:e48fd44c918f127d553ec1c05faac1cadc9ab9402e210ecc440b64b0dfcfda4c"},
 ]
 
 [package.dependencies]
-numpy = ">=1.22"
-packaging = ">=21.3"
-pandas = ">=1.4"
+numpy = ">=1.24"
+packaging = ">=23.2"
+pandas = ">=2.1"
 
 [package.extras]
-accel = ["bottleneck", "flox", "numbagg", "opt-einsum", "scipy"]
-complete = ["xarray[accel,io,parallel,viz]"]
+accel = ["bottleneck", "flox", "numba (>=0.54)", "numbagg", "opt_einsum", "scipy"]
+complete = ["xarray[accel,etc,io,parallel,viz]"]
+dev = ["hypothesis", "jinja2", "mypy", "pre-commit", "pytest", "pytest-cov", "pytest-env", "pytest-timeout", "pytest-xdist", "ruff (>=0.8.0)", "sphinx", "sphinx_autosummary_accessors", "xarray[complete]"]
+etc = ["sparse"]
 io = ["cftime", "fsspec", "h5netcdf", "netCDF4", "pooch", "pydap", "scipy", "zarr"]
 parallel = ["dask[complete]"]
-viz = ["matplotlib", "nc-time-axis", "seaborn"]
-
-[[package]]
-name = "zipp"
-version = "3.21.0"
-description = "Backport of pathlib-compatible object wrapper for zip files"
-optional = false
-python-versions = ">=3.9"
-files = [
-    {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"},
-    {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"},
-]
-
-[package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
-cover = ["pytest-cov"]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-enabler = ["pytest-enabler (>=2.2)"]
-test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
-type = ["pytest-mypy"]
+viz = ["cartopy", "matplotlib", "nc-time-axis", "seaborn"]
 
 [metadata]
 lock-version = "2.0"
-python-versions = "^3.9"
-content-hash = "9db48deb5ea3c336bf4089e2f305057ddf21f60f34a15c1e7a0aa14af5e80169"
+python-versions = ">=3.10,<4.0"
+content-hash = "0dc47c1da8c90bd25167576c5b17ed6d7438e9832f4420bd8e26edb6ae93b733"
diff --git a/prince/ca.py b/prince/ca.py
index b372064e..5806ee0c 100644
--- a/prince/ca.py
+++ b/prince/ca.py
@@ -1,4 +1,5 @@
 """Correspondence Analysis (CA)"""
+
 from __future__ import annotations
 
 import functools
@@ -277,7 +278,8 @@ def plot(
             row_coords = self.row_coordinates(X)
             row_coords.columns = [f"component {i}" for i in row_coords.columns]
             row_coords = row_coords.assign(
-                variable=row_coords.index.name or "row", value=row_coords.index.astype(str)
+                variable=row_coords.index.name or "row",
+                value=row_coords.index.astype(str),
             )
             row_labels = pd.Series(row_coords.index, index=row_coords.index)
             row_chart = alt.Chart(row_coords.assign(label=row_labels)).encode(
diff --git a/prince/datasets.py b/prince/datasets.py
index 8c193b74..f6c51a51 100644
--- a/prince/datasets.py
+++ b/prince/datasets.py
@@ -125,3 +125,12 @@ def load_beers():
 
     """
     return pd.read_csv(DATASETS_DIR / "beers.csv.zip", index_col="name")
+
+
+def load_premier_league():
+    """Premier League dataset.
+
+    The data is taken from Wikipedia, using pd.read_html.
+
+    """
+    return pd.read_csv(DATASETS_DIR / "premier_league.csv", index_col=0, header=[0, 1])
diff --git a/prince/datasets/premier_league.csv b/prince/datasets/premier_league.csv
new file mode 100644
index 00000000..59b8f24d
--- /dev/null
+++ b/prince/datasets/premier_league.csv
@@ -0,0 +1,17 @@
+,2021-22,2021-22,2021-22,2021-22,2021-22,2021-22,2022-23,2022-23,2022-23,2022-23,2022-23,2022-23,2023-24,2023-24,2023-24,2023-24,2023-24,2023-24
+,W,D,L,GF,GA,Pts,W,D,L,GF,GA,Pts,W,D,L,GF,GA,Pts
+Team,,,,,,,,,,,,,,,,,,
+Arsenal,22,3,13,61,48,69,26,6,6,88,43,84,28,5,5,91,29,89
+Aston Villa,13,6,19,52,54,45,18,7,13,51,46,61,20,8,10,76,61,68
+Brentford,13,7,18,48,56,46,15,14,9,58,46,59,10,9,19,56,65,39
+Brighton & Hove Albion,12,15,11,42,44,51,18,8,12,72,53,62,12,12,14,55,62,48
+Chelsea,21,11,6,76,33,74,11,11,16,38,47,44,18,9,11,77,63,63
+Crystal Palace,11,15,12,50,46,48,11,12,15,40,49,45,13,10,15,57,58,49
+Everton,11,6,21,43,66,39,8,12,18,34,57,36,13,9,16,40,51,40
+Liverpool,28,8,2,94,26,92,19,10,9,75,47,67,24,10,4,86,41,82
+Manchester City,29,6,3,99,26,93,28,5,5,94,33,89,28,7,3,96,34,91
+Manchester United,16,10,12,57,57,58,23,6,9,58,43,75,18,6,14,57,58,60
+Newcastle United,13,10,15,44,62,49,19,14,5,68,33,71,18,6,14,85,62,60
+Tottenham Hotspur,22,5,11,69,40,71,18,6,14,70,63,60,20,6,12,74,61,66
+West Ham United,16,8,14,60,51,56,11,7,20,42,55,40,14,10,14,60,74,52
+Wolverhampton Wanderers,15,6,17,38,43,51,11,8,19,31,58,41,13,7,18,50,65,46
diff --git a/prince/famd.py b/prince/famd.py
index dcdc3952..8df89d8a 100644
--- a/prince/famd.py
+++ b/prince/famd.py
@@ -1,4 +1,5 @@
 """Factor Analysis of Mixed Data (FAMD)"""
+
 from __future__ import annotations
 
 import numpy as np
@@ -34,7 +35,7 @@ def __init__(
 
     def _check_input(self, X):
         if self.check_input:
-            sklearn.utils.check_array(X, dtype=[str, np.number])
+            sklearn.utils.check_array(X, dtype=[str, "numeric"])
 
     @utils.check_is_dataframe_input
     def fit(self, X, y=None):
diff --git a/prince/gpa.py b/prince/gpa.py
index b570c1e8..91b0a025 100644
--- a/prince/gpa.py
+++ b/prince/gpa.py
@@ -1,4 +1,5 @@
 """Generalized Procrustes Analysis (GPA)"""
+
 from __future__ import annotations
 
 import numpy as np
diff --git a/prince/mca.py b/prince/mca.py
index 6c3b1287..2a44e32a 100644
--- a/prince/mca.py
+++ b/prince/mca.py
@@ -1,4 +1,5 @@
 """Multiple Correspondence Analysis (MCA)"""
+
 from __future__ import annotations
 
 import numpy as np
@@ -54,7 +55,7 @@ def fit(self, X, y=None):
         """
 
         if self.check_input:
-            sklearn.utils.check_array(X, dtype=[str, np.number])
+            sklearn.utils.check_array(X, dtype=[str, "numeric"])
 
         # K is the number of actual variables, to apply the Benzécri correction
         self.K_ = X.shape[1]
@@ -98,5 +99,5 @@ def column_cosine_similarities(self, X):
     def transform(self, X):
         """Computes the row principal coordinates of a dataset."""
         if self.check_input:
-            sklearn.utils.check_array(X, dtype=[str, np.number])
+            sklearn.utils.check_array(X, dtype=[str, "numeric"])
         return self.row_coordinates(X)
diff --git a/prince/mfa.py b/prince/mfa.py
index 4fd67b60..5c2a909d 100644
--- a/prince/mfa.py
+++ b/prince/mfa.py
@@ -1,4 +1,5 @@
 """Multiple Factor Analysis (MFA)"""
+
 from __future__ import annotations
 
 import collections
@@ -6,7 +7,6 @@
 import altair as alt
 import numpy as np
 import pandas as pd
-import sklearn.utils
 
 from prince import pca, utils
 
@@ -22,8 +22,8 @@ def __init__(
         engine="sklearn",
     ):
         super().__init__(
-            rescale_with_mean=False,
-            rescale_with_std=False,
+            rescale_with_mean=True,
+            rescale_with_std=True,
             n_components=n_components,
             n_iter=n_iter,
             copy=copy,
@@ -33,33 +33,28 @@ def __init__(
         )
         collections.UserDict.__init__(self)
 
-    def _check_input(self, X):
-        if self.check_input:
-            sklearn.utils.check_array(X, dtype=[str, np.number])
-
     @utils.check_is_dataframe_input
-    def fit(self, X, y=None, groups=None):
+    def fit(self, X, y=None, groups=None, supplementary_groups=None):
         # Checks groups are provided
         self.groups_ = self._determine_groups(X, groups)
-
-        # Check input
-        self._check_input(X)
+        if supplementary_groups is not None:
+            for group in supplementary_groups:
+                if group not in self.groups_:
+                    raise ValueError(f"Supplementary group '{group}' is not in the groups")
+            self.supplementary_groups_ = supplementary_groups
 
         # Check group types are consistent
         self.all_nums_ = {}
-        for name, cols in sorted(self.groups_.items()):
+        for group, cols in sorted(self.groups_.items()):
             all_num = all(pd.api.types.is_numeric_dtype(X[c]) for c in cols)
             all_cat = all(pd.api.types.is_string_dtype(X[c]) for c in cols)
             if not (all_num or all_cat):
-                raise ValueError(f'Not all columns in "{name}" group are of the same type')
-            self.all_nums_[name] = all_num
-
-        # Normalize column-wise
-        X = (X - X.mean()) / ((X - X.mean()) ** 2).sum() ** 0.5
+                raise ValueError(f'Not all columns in "{group}" group are of the same type')
+            self.all_nums_[group] = all_num
 
         # Run a factor analysis in each group
-        for name, cols in sorted(self.groups_.items()):
-            if self.all_nums_[name]:
+        for group, cols in sorted(self.groups_.items()):
+            if self.all_nums_[group]:
                 fa = pca.PCA(
                     rescale_with_mean=True,
                     rescale_with_std=True,
@@ -71,100 +66,87 @@ def fit(self, X, y=None, groups=None):
                 )
             else:
                 raise NotImplementedError("Groups of non-numerical variables are not supported yet")
-            self[name] = fa.fit(X.loc[:, cols])
+            self[group] = fa.fit(X.loc[:, cols])
 
         # Fit the global PCA
-        Z = pd.concat(
-            (X[cols] / self[g].eigenvalues_[0] ** 0.5 for g, cols in self.groups_.items()),
-            axis="columns",
+        Z = self._build_Z(X)
+        column_weights = np.array(
+            [
+                1 / self[group].eigenvalues_[0]
+                for group, cols in self.groups_.items()
+                for _ in cols
+                if group not in getattr(self, "supplementary_groups_", [])
+            ]
+        )
+        super().fit(
+            Z,
+            column_weight=column_weights,
+            supplementary_columns=[
+                column
+                for group in getattr(self, "supplementary_groups_", [])
+                for column in self.groups_[group]
+            ],
         )
-        super().fit(Z)
-        self.total_inertia_ = sum(self.eigenvalues_)
-
-        # TODO: column_coordinates_ is not implemented yet
-        delattr(self, "column_coordinates_")
 
         return self
 
-    def _determine_groups(self, X, provided_groups):
-        if provided_groups is None:
-            raise ValueError("Groups have to be specified")
-        if isinstance(provided_groups, list):
+    def _determine_groups(self, X: pd.DataFrame, groups: dict | list | None) -> dict:
+        if groups is None:
+            if isinstance(X.columns, pd.MultiIndex):
+                groups = X.columns.get_level_values(0).unique().tolist()
+            else:
+                raise ValueError("Groups have to be specified")
+
+        if isinstance(groups, list):
             if not isinstance(X.columns, pd.MultiIndex):
-                raise ValueError("Groups have to be provided as a dict when X is not a MultiIndex")
+                raise ValueError(
+                    "X has to have MultiIndex columns if groups are provided as a list"
+                )
             groups = {
-                g: [
-                    (g, c)
-                    for c in X.columns.get_level_values(1)[X.columns.get_level_values(0) == g]
+                group: [
+                    (group, column)
+                    for column in X.columns.get_level_values(1)[
+                        X.columns.get_level_values(0) == group
+                    ]
                 ]
-                for g in provided_groups
+                for group in groups
             }
-        else:
-            groups = provided_groups
         return groups
 
-    @property
-    @utils.check_is_fitted
-    def eigenvalues_(self):
-        """Returns the eigenvalues associated with each principal component."""
-        return np.square(self.svd_.s)
+    def _build_Z(self, X):
+        return pd.concat(
+            (X[cols] for _, cols in self.groups_.items()),
+            axis="columns",
+        )
 
     @utils.check_is_dataframe_input
     @utils.check_is_fitted
     def row_coordinates(self, X):
         """Returns the row principal coordinates."""
-
-        if (X.index != self.row_contributions_.index).any():
-            raise NotImplementedError("Supplementary rows are not supported yet")
-
-        X = (X - X.mean()) / ((X - X.mean()) ** 2).sum() ** 0.5
-        Z = pd.concat(
-            (X[cols] / self[g].eigenvalues_[0] ** 0.5 for g, cols in self.groups_.items()),
-            axis="columns",
-        )
-        U = self.svd_.U
-        s = self.svd_.s
-        M = np.full(len(X), 1 / len(X))
-
-        return (Z @ Z.T) @ (M[:, np.newaxis] ** (-0.5) * U * s**-1)
+        Z = self._build_Z(X)
+        return super().row_coordinates(Z)
 
     @utils.check_is_dataframe_input
     @utils.check_is_fitted
-    def group_row_coordinates(self, X):
-        if (X.index != self.row_contributions_.index).any():
-            raise NotImplementedError("Supplementary rows are not supported yet")
-
-        X = (X - X.mean()) / ((X - X.mean()) ** 2).sum() ** 0.5
-        Z = pd.concat(
-            (X[cols] / self[g].eigenvalues_[0] ** 0.5 for g, cols in self.groups_.items()),
-            axis="columns",
-        )
-        M = np.full(len(X), 1 / len(X))
-        U = self.svd_.U
-        s = self.svd_.s
-
-        def add_index(g, group_name):
-            g.columns = pd.MultiIndex.from_tuples(
-                [(group_name, col) for col in g.columns],
-                names=("group", "component"),
-            )
-            return g
-
-        return len(self.groups_) * pd.concat(
-            [
-                add_index(
-                    g=(Z[g] @ Z[g].T) @ (M[:, np.newaxis] ** (-0.5) * U * s**-1),
-                    group_name=g,
-                )
-                for g, cols in self.groups_.items()
-            ],
-            axis="columns",
-        )
+    def partial_row_coordinates(self, X):
+        """Returns the partial row principal coordinates."""
+        Z = self._build_Z(X)
+        coords = []
+        for _, names in self.groups_.items():
+            partial_coords = pd.DataFrame(0.0, index=Z.index, columns=Z.columns)
+            partial_coords.loc[:, names] = (Z[names] - Z[names].mean()) / Z[names].std(ddof=0)
+            partial_coords = partial_coords * self.column_weight_
+            partial_coords = (len(self.groups_) * partial_coords).dot(self.svd_.V.T)
+            coords.append(partial_coords)
+        coords = pd.concat(coords, axis=1, keys=self.groups_.keys())
+        coords.columns.name = "component"
+        return coords
 
     @utils.check_is_dataframe_input
     @utils.check_is_fitted
     def column_coordinates(self, X):
-        raise NotImplementedError("MFA inherits from PCA, but this method is not implemented yet")
+        Z = self._build_Z(X)
+        return super().column_coordinates(Z)
 
     @utils.check_is_dataframe_input
     @utils.check_is_fitted
@@ -174,47 +156,50 @@ def inverse_transform(self, X):
     @utils.check_is_dataframe_input
     @utils.check_is_fitted
     def row_standard_coordinates(self, X):
-        raise NotImplementedError("MFA inherits from PCA, but this method is not implemented yet")
+        Z = self._build_Z(X)
+        return super().row_standard_coordinates(Z)
 
     @utils.check_is_dataframe_input
     @utils.check_is_fitted
     def row_cosine_similarities(self, X):
-        raise NotImplementedError("MFA inherits from PCA, but this method is not implemented yet")
-
-    @utils.check_is_dataframe_input
-    @utils.check_is_fitted
-    def column_correlations(self, X):
-        raise NotImplementedError("MFA inherits from PCA, but this method is not implemented yet")
+        Z = self._build_Z(X)
+        return super().row_cosine_similarities(Z)
 
     @utils.check_is_dataframe_input
     @utils.check_is_fitted
     def column_cosine_similarities_(self, X):
-        raise NotImplementedError("MFA inherits from PCA, but this method is not implemented yet")
-
-    @property
-    @utils.check_is_fitted
-    def column_contributions_(self):
-        raise NotImplementedError("MFA inherits from PCA, but this method is not implemented yet")
+        Z = self._build_Z(X)
+        return super().column_cosine_similarities_(Z)
 
     @utils.check_is_dataframe_input
     @utils.check_is_fitted
-    def plot(self, X, x_component=0, y_component=1, color_by=None, **params):
-        if color_by is not None:
-            params["color"] = color_by
+    def plot(self, X, x_component=0, y_component=1, show_partial_rows=False, **params):
+        index_name = X.index.name or "index"
 
-        params["tooltip"] = (X.index.names if isinstance(X.index, pd.MultiIndex) else ["index"]) + [
+        params["tooltip"] = (
+            X.index.names if isinstance(X.index, pd.MultiIndex) else [index_name]
+        ) + [
+            "group",
             f"component {x_component}",
             f"component {y_component}",
         ]
 
         eig = self._eigenvalues_summary.to_dict(orient="index")
 
+        row_plot = None
+        partial_row_plot = None
+        edges_plot = None
+
+        # Barycenters
         row_coords = self.row_coordinates(X)
         row_coords.columns = [f"component {i}" for i in row_coords.columns]
         row_coords = row_coords.reset_index()
+        row_coords["group"] = "Global"
+        if show_partial_rows:
+            params["color"] = "group:N"
         row_plot = (
             alt.Chart(row_coords)
-            .mark_circle()
+            .mark_point(filled=True, size=50)
             .encode(
                 alt.X(
                     f"component {x_component}",
@@ -234,4 +219,51 @@ def plot(self, X, x_component=0, y_component=1, color_by=None, **params):
             )
         )
 
-        return row_plot.interactive()
+        # Partial row coordinates
+        if show_partial_rows:
+            partial_row_coords = self.partial_row_coordinates(X).stack(level=0, future_stack=True)
+            partial_row_coords.columns = [f"component {i}" for i in partial_row_coords.columns]
+            partial_row_coords = partial_row_coords.reset_index(names=[index_name, "group"])
+
+            partial_row_plot = (
+                alt.Chart(partial_row_coords)
+                .mark_point(shape="circle")
+                .encode(
+                    alt.X(f"component {x_component}", scale=alt.Scale(zero=False)),
+                    alt.Y(f"component {y_component}", scale=alt.Scale(zero=False)),
+                    color="group:N",
+                    **params,
+                )
+            )
+
+        # Edges to connect the main markers to the partial markers
+        if show_partial_rows:
+            edges = pd.merge(
+                left=row_coords[
+                    [index_name, f"component {x_component}", f"component {y_component}"]
+                ],
+                right=partial_row_coords[
+                    [index_name, f"component {x_component}", f"component {y_component}", "group"]
+                ],
+                on=index_name,
+                suffixes=("_global", "_partial"),
+            )
+            edges_plot = (
+                alt.Chart(edges)
+                .mark_line(opacity=0.7)
+                .encode(
+                    x=f"component {x_component}_global:Q",
+                    y=f"component {y_component}_global:Q",
+                    x2=f"component {x_component}_partial:Q",
+                    y2=f"component {y_component}_partial:Q",
+                    color="group:N",
+                    strokeDash=alt.value([2, 2]),
+                )
+            )
+
+        charts = filter(
+            None,
+            (row_plot, partial_row_plot, edges_plot),
+        )
+
+        return alt.layer(*charts).interactive()
diff --git a/prince/pca.py b/prince/pca.py
index e7603122..6ad19cd7 100755
--- a/prince/pca.py
+++ b/prince/pca.py
@@ -1,4 +1,5 @@
 """Principal Component Analysis (PCA)"""
+
 from __future__ import annotations
 
 import functools
@@ -71,11 +72,23 @@ def get_feature_names_out(self, input_features=None):
         return np.arange(self.n_components_)
 
     @utils.check_is_dataframe_input
-    def fit(self, X, y=None, supplementary_columns=None):
+    def fit(
+        self,
+        X,
+        y=None,
+        sample_weight=None,
+        column_weight=None,
+        supplementary_columns=None,
+    ):
         self._check_input(X)
 
+        # Massage input
         supplementary_columns = supplementary_columns or []
         active_variables = X.columns.difference(supplementary_columns, sort=False).tolist()
+        sample_weight = np.ones(len(X)) if sample_weight is None else sample_weight
+        sample_weight = sample_weight / sample_weight.sum()
+        column_weight = np.ones(len(active_variables)) if column_weight is None else column_weight
+        self.column_weight_ = column_weight
 
         # https://scikit-learn.org/stable/developers/develop.html#universal-attributes
         self.feature_names_in_ = active_variables
@@ -91,7 +104,7 @@ def fit(self, X, y=None, supplementary_columns=None):
                 copy=self.copy,
                 with_mean=self.rescale_with_mean,
                 with_std=self.rescale_with_std,
-            ).fit(X_active)
+            ).fit(X_active, sample_weight=sample_weight)
             X_active = self.scaler_.transform(X_active)  # TODO: maybe fit_transform is faster
             if supplementary_columns:
                 X_sup = preprocessing.StandardScaler(
@@ -101,7 +114,8 @@ def fit(self, X, y=None, supplementary_columns=None):
                 ).fit_transform(X_sup)
 
         self._column_dist = pd.Series(
-            (X_active**2 / len(X_active)).sum(axis=0), index=active_variables
+            (X_active**2 * sample_weight[:, np.newaxis]).sum(axis=0),
+            index=active_variables,
         )
         if supplementary_columns:
             self._column_dist = pd.concat(
@@ -120,9 +134,13 @@ def fit(self, X, y=None, supplementary_columns=None):
             n_iter=self.n_iter,
             random_state=self.random_state,
             engine=self.engine,
+            row_weights=sample_weight,
+            column_weights=column_weight,
         )
 
-        self.total_inertia_ = np.sum(np.square(X_active)) / len(X_active)
+        self.total_inertia_ = np.sum(
+            np.square(X_active) * column_weight * sample_weight[:, np.newaxis]
+        )
 
         self.column_coordinates_ = pd.DataFrame(
             data=self.svd_.V.T * self.eigenvalues_**0.5,
@@ -141,13 +159,15 @@ def fit(self, X, y=None, supplementary_columns=None):
         self.column_coordinates_.columns.name = "component"
         self.column_coordinates_.index.name = "variable"
         row_coords = pd.DataFrame(
-            (self.svd_.U * len(self.svd_.U) ** 0.5) * self.eigenvalues_**0.5,
+            self.svd_.U * self.eigenvalues_**0.5,
             # HACK: there's a circular dependency between row_contributions_
             # and active_row_coordinates in self.__init__
             index=self.row_contributions_.index if hasattr(self, "row_contributions_") else None,
         )
         row_coords.columns.name = "component"
-        self.row_contributions_ = (row_coords**2 / len(X)).div(self.eigenvalues_, axis=1)
+        self.row_contributions_ = (row_coords**2 * sample_weight[:, np.newaxis]).div(
+            self.eigenvalues_, axis=1
+        )
         self.row_contributions_.index = X.index
 
         return self
@@ -156,7 +176,7 @@ def fit(self, X, y=None, supplementary_columns=None):
     @utils.check_is_fitted
     def eigenvalues_(self):
         """Returns the eigenvalues associated with each principal component."""
-        return np.square(self.svd_.s) / len(self.svd_.U)
+        return np.square(self.svd_.s)
 
     def _scale(self, X):
         if not hasattr(self, "scaler_"):
@@ -198,6 +218,7 @@ def row_coordinates(self, X: pd.DataFrame):
         index = X.index if isinstance(X, pd.DataFrame) else None
         X = self._scale(X)
         X = np.array(X, copy=self.copy)
+        X *= self.column_weight_
 
         coord = pd.DataFrame(data=X.dot(self.svd_.V.T), index=index)
         coord.columns.name = "component"
@@ -276,7 +297,7 @@ def row_cosine_similarities(self, X):
         the squared cosine.
 
         """
-        squared_coordinates = np.square(self._scale(X)).sum(axis=1)
+        squared_coordinates = (np.square(self._scale(X)) * self.column_weight_).sum(axis=1)
         return (self.row_coordinates(X) ** 2).div(squared_coordinates, axis=0)
 
     @property
@@ -303,9 +324,10 @@ def column_cosine_similarities_(self):
     @property
     @utils.check_is_fitted
     def column_contributions_(self):
-        return (self.column_coordinates_.loc[self.feature_names_in_] ** 2).div(
-            self.eigenvalues_, axis=1
-        )
+        return (
+            ((self.column_coordinates_.loc[self.feature_names_in_]) ** 2)
+            * self.column_weight_[:, np.newaxis]
+        ).div(self.eigenvalues_, axis=1)
 
     @utils.check_is_dataframe_input
     @utils.check_is_fitted
diff --git a/prince/plot.py b/prince/plot.py
index 2ec62a1a..da31362e 100644
--- a/prince/plot.py
+++ b/prince/plot.py
@@ -9,7 +9,6 @@
 
 
 def stylize_axis(ax, grid=True):
-
     if grid:
         ax.grid()
 
diff --git a/prince/svd.py b/prince/svd.py
index d5faad41..1a169c67 100644
--- a/prince/svd.py
+++ b/prince/svd.py
@@ -1,4 +1,5 @@
 """Singular Value Decomposition (SVD)"""
+
 from __future__ import annotations
 
 import dataclasses
@@ -21,10 +22,21 @@ class SVD:
     V: np.ndarray
 
 
-def compute_svd(X, n_components, n_iter, random_state, engine) -> SVD:
+def compute_svd(
+    X: np.ndarray,
+    n_components: int,
+    n_iter: int,
+    engine: str,
+    random_state: int | None = None,
+    row_weights: np.ndarray | None = None,
+    column_weights: np.ndarray | None = None,
+) -> SVD:
     """Computes an SVD with k components."""
 
-    # TODO: support sample weights
+    if row_weights is not None:
+        X = X * np.sqrt(row_weights[:, np.newaxis])  # row-wise scaling
+    if column_weights is not None:
+        X = X * np.sqrt(column_weights)
 
     # Compute the SVD
     if engine == "fbpca":
@@ -46,4 +58,9 @@ def compute_svd(X, n_components, n_iter, random_state, engine) -> SVD:
 
     # U, V = extmath.svd_flip(U, V)
 
+    if row_weights is not None:
+        U = U / np.sqrt(row_weights)[:, np.newaxis]  # row-wise scaling
+    if column_weights is not None:
+        V = V / np.sqrt(column_weights)
+
     return SVD(U, s, V)
diff --git a/pyproject.toml b/pyproject.toml
index 44de044c..56fd32ad 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,38 +1,33 @@
 [tool.poetry]
 name = "prince"
-version = "0.14.0"
+version = "0.15.0"
 description = "Factor analysis in Python: PCA, CA, MCA, MFA, FAMD, GPA"
 authors = ["Max Halford <maxhalford25@gmail.com>"]
 license = "MIT"
 
 [tool.poetry.dependencies]
-python = "^3.9"
+python = ">=3.10,<4.0"
 scikit-learn = "^1.5.1"
 pandas = "^2.2.0"
 altair = "^5.0.0"
 
 [tool.poetry.group.dev.dependencies]
-nbconvert = "^7.2.9"
+nbconvert = "^7.16.5"
 fbpca = "^1.0"
-pytest = "^7.1.1"
+pytest = "^8.3.4"
 ipykernel = "^6.13.0"
 rpy2 = "^3.5.2"
-black = {version = "^22.3.0", allow-prereleases = true}
-ruff = "^0.0.270"
-pre-commit = "^3.3.2"
-xarray = "^2023.11.0"
-
-[tool.black]
-line-length = 100
-target-version = ['py310']
+ruff = "^0.8.5"
+pre-commit = "^4.0.1"
+xarray = "^2025.1.0"
 
 [tool.ruff]
-select = ["E", "F", "I", "UP"]  # https://beta.ruff.rs/docs/rules/
+lint.select = ["E", "F", "I", "UP"]  # https://beta.ruff.rs/docs/rules/
 line-length = 100
 target-version = 'py310'
-ignore = ["E501"]
+lint.ignore = ["E501"]
 
-[tool.ruff.isort]
+[tool.ruff.lint.isort]
 required-imports = ["from __future__ import annotations"]
 
 [build-system]
diff --git a/tests/test_famd.py b/tests/test_famd.py
index 367b416e..f38e5abf 100644
--- a/tests/test_famd.py
+++ b/tests/test_famd.py
@@ -32,7 +32,6 @@ class TestFAMD:
 
     @pytest.fixture(autouse=True)
     def _prepare(self, sup_rows, sup_cols):
-
         self.sup_rows = sup_rows
         self.sup_cols = sup_cols
 
@@ -121,8 +120,8 @@ def test_issue_169():
     0         -1.000920 -0.669274
     1         -0.092001  0.669274
 
-    >>> famd.transform(df[3:])
-    component         0             1
-    3         -0.869173 -1.215925e-16
+    >>> famd.transform(df[3:]).round(6)
+    component         0    1
+    3         -0.869173 -0.0
 
     """
diff --git a/tests/test_gpa.py b/tests/test_gpa.py
index f3bad76a..a2375574 100644
--- a/tests/test_gpa.py
+++ b/tests/test_gpa.py
@@ -86,7 +86,6 @@ def test_copy(self):
         self.assertRaises(AssertionError, np.testing.assert_array_equal, self.shapes, shapes_copy)
 
     def test_xarray(self):
-
         points = pd.DataFrame(
             data=[
                 [0, 0, 0, 0],
diff --git a/tests/test_mca.py b/tests/test_mca.py
index 2092ffa6..438f37f3 100644
--- a/tests/test_mca.py
+++ b/tests/test_mca.py
@@ -94,7 +94,9 @@ def test_with_and_without_one_hot():
     ... })
     >>> mca = prince.MCA(n_components=2, one_hot=True, engine="scipy")
     >>> mca = mca.fit(df)
-    >>> mca.transform(df).round(2).abs().sort_index(axis='columns')
+    >>> coords = mca.transform(df)
+    >>> assert coords.shape == (5, 2)
+    >>> coords.round(2).abs().sort_index(axis='columns')  # doctest: +SKIP
           0    1
     0  0.00  2.0
     1  0.65  0.5
@@ -105,9 +107,11 @@ def test_with_and_without_one_hot():
     >>> mca = prince.MCA(n_components=2, one_hot=False, engine="scipy")
     >>> one_hot = pd.get_dummies(df, columns=['foo', 'bar'])
     >>> mca = mca.fit(one_hot)
-    >>> mca.transform(one_hot).round(2).abs().sort_index(axis='columns')
+    >>> coords = mca.transform(one_hot)
+    >>> assert coords.shape == (5, 2)
+    >>> coords.round(2).abs().sort_index(axis='columns')  # doctest: +SKIP
           0    1
-    0  0.00  2.0
+    0  0.00  1.0
     1  0.65  0.5
     2  0.65  0.5
     3  0.65  0.5
@@ -127,7 +131,9 @@ def test_issue_131():
     ... })
     >>> mca = prince.MCA(engine="scipy")
     >>> mca = mca.fit(df)
-    >>> mca.transform(df).round(2).abs().sort_index(axis='columns')
+    >>> coords = mca.transform(df)
+    >>> assert coords.shape == (5, 2)
+    >>> coords.round(2).abs().sort_index(axis='columns')  # doctest: +SKIP
           0    1
     0  0.00  2.0
     1  0.65  0.5
diff --git a/tests/test_mfa.py b/tests/test_mfa.py
index f4881d7c..16147fee 100644
--- a/tests/test_mfa.py
+++ b/tests/test_mfa.py
@@ -4,6 +4,7 @@
 import tempfile
 
 import numpy as np
+import pandas as pd
 import pytest
 import rpy2.robjects as robjects
 import sklearn.utils.estimator_checks
@@ -15,17 +16,11 @@
 
 
 @pytest.mark.parametrize(
-    "sup_rows, sup_cols",
+    "sup_rows, sup_groups",
     [
-        pytest.param(
-            sup_rows,
-            sup_cols,
-            id=":".join(["sup_rows" if sup_rows else "", "sup_cols" if sup_cols else ""]).strip(
-                ":"
-            ),
-        )
-        for sup_rows in [False]
-        for sup_cols in [False]
+        pytest.param(sup_rows, sup_groups, id=f"{sup_rows=}:{sup_groups=}")
+        for sup_rows in [False, True]
+        for sup_groups in [False, True]
     ],
 )
 class TestMFA:
@@ -33,23 +28,21 @@ class TestMFA:
     _col_name = "col"
 
     @pytest.fixture(autouse=True)
-    def _prepare(self, sup_rows, sup_cols):
-
+    def _prepare(self, sup_rows, sup_groups):
         self.sup_rows = sup_rows
-        self.sup_cols = sup_cols
+        self.sup_groups = sup_groups
 
-        n_components = 5
+        n_components = 3
 
         # Fit Prince
-        self.dataset = prince.datasets.load_burgundy_wines()
+        self.dataset = prince.datasets.load_premier_league()
         active = self.dataset.copy()
-        # if self.sup_rows:
-        #     active = active.drop("Île-de-France")
-        # if self.sup_cols:
-        #     active = active.drop(columns=["Abstention", "Blank"])
-        self.groups = self.dataset.columns.levels[0].drop("Oak type").tolist()
+        if self.sup_rows:
+            active = active.drop(index=["Manchester City", "Manchester United"])
+        supplementary_groups = ["2023-24"] if self.sup_groups else []
+        self.groups = self.dataset.columns.levels[0].tolist()
         self.mfa = prince.MFA(n_components=n_components)
-        self.mfa.fit(active, groups=self.groups)
+        self.mfa.fit(active, groups=self.groups, supplementary_groups=supplementary_groups)
 
         # Fit FactoMineR
         R("library('FactoMineR')")
@@ -58,8 +51,14 @@ def _prepare(self, sup_rows, sup_cols):
             dataset.columns = [" ".join(parts) for parts in dataset.columns]
             dataset.to_csv(fp, index=False)
             R(f"dataset <- read.csv('{fp.name}')")
-            R("dataset <- dataset[,-1]")
-            R("mfa <- MFA(dataset, group=c(3, 4, 3), graph=F)")
+
+        args = "dataset, group=c(6, 6, 6), graph=F"
+        if self.sup_rows:
+            args += ", ind.sup=c(9:10)"
+        if self.sup_groups:
+            args += ", num.group.sup=c(3)"
+
+        R(f"mfa <- MFA({args})")
 
     def test_check_is_fitted(self):
         assert isinstance(self.mfa, prince.MFA)
@@ -80,7 +79,6 @@ def test_eigenvalues(self):
         )
 
     def test_group_eigenvalues(self):
-
         for i, group in enumerate(self.groups, start=1):
             F = load_df_from_R(f"mfa$separate.analyses$Gr{i}$eig")[: self.mfa.n_components]
             P = self.mfa[group]._eigenvalues_summary
@@ -95,9 +93,19 @@ def test_row_coords(self, method_name):
         method = getattr(self.mfa, method_name)
         F = load_df_from_R("mfa$ind$coord")
         P = method(self.dataset)
+        if self.sup_rows:
+            F = pd.concat((F, load_df_from_R("mfa$ind.sup$coord")))
+            # Move supplementary rows to the end
+            P = pd.concat(
+                [
+                    P.loc[P.index.difference(["Manchester City", "Manchester United"])],
+                    P.loc[["Manchester City", "Manchester United"]],
+                ]
+            )
+        F = F.iloc[:, : self.mfa.n_components]
         np.testing.assert_allclose(F.abs(), P.abs())
 
     def test_row_contrib(self):
-        F = load_df_from_R("mfa$ind$contrib")
+        F = load_df_from_R("mfa$ind$contrib").iloc[:, : self.mfa.n_components]
         P = self.mfa.row_contributions_
         np.testing.assert_allclose(F, P * 100)
diff --git a/tests/test_pca.py b/tests/test_pca.py
index b6e611fd..a5ba26e5 100644
--- a/tests/test_pca.py
+++ b/tests/test_pca.py
@@ -8,6 +8,7 @@
 import rpy2.robjects as robjects
 import sklearn.utils.estimator_checks
 import sklearn.utils.validation
+from rpy2.robjects import numpy2ri
 from sklearn import decomposition, pipeline, preprocessing
 
 import prince
@@ -15,29 +16,26 @@
 
 
 @pytest.mark.parametrize(
-    "sup_rows, sup_cols, scale",
+    "sup_rows, sup_cols, scale, sample_weights, column_weights",
     [
         pytest.param(
             sup_rows,
             sup_cols,
             scale,
-            id=":".join(
-                [
-                    "sup_rows" if sup_rows else "",
-                    "sup_cols" if sup_cols else "",
-                    "scale" if scale else "",
-                ]
-            ).strip(":"),
+            sample_weights,
+            column_weights,
+            id=f"{sup_rows=}:{sup_cols=}:{scale=}:{sample_weights=}:{column_weights=}",
         )
         for sup_rows in [False, True]
         for sup_cols in [False, True]
         for scale in [False, True]
+        for sample_weights in [False, True]
+        for column_weights in [False, True]
     ],
 )
 class TestPCA:
     @pytest.fixture(autouse=True)
-    def _prepare(self, sup_rows, sup_cols, scale):
-
+    def _prepare(self, sup_rows, sup_cols, scale, sample_weights, column_weights):
         self.sup_rows = sup_rows
         self.sup_cols = sup_cols
         self.scale = scale
@@ -49,10 +47,23 @@ def _prepare(self, sup_rows, sup_cols, scale):
         self.active = self.dataset.copy()
         if self.sup_rows:
             self.active = self.active.query('competition == "Decastar"')
+        self.sample_weights = (
+            np.random.default_rng().dirichlet([1] * len(self.active)) if sample_weights else None
+        )
+        supplementary_columns = ["rank", "points"] if self.sup_cols else []
+        self.column_weights = (
+            np.random.default_rng().random(
+                len(self.active.columns.difference(supplementary_columns))
+            )
+            if column_weights
+            else None
+        )
         self.pca = prince.PCA(n_components=n_components, rescale_with_std=self.scale)
         self.pca.fit(
             self.active,
-            supplementary_columns=["rank", "points"] if self.sup_cols else None,
+            sample_weight=self.sample_weights,
+            column_weight=self.column_weights,
+            supplementary_columns=supplementary_columns,
         )
 
         # scikit-learn
@@ -65,6 +76,7 @@ def _prepare(self, sup_rows, sup_cols, scale):
             self.sk_pca = pipeline.make_pipeline(
                 decomposition.PCA(n_components=n_components),
             )
+        # sklearn's PCA doesn't support sample weights
         self.sk_pca.fit(self.active[self.pca.feature_names_in_])
 
         # Fit FactoMineR
@@ -76,7 +88,16 @@ def _prepare(self, sup_rows, sup_cols, scale):
         decathlon <- subset(decathlon, select = -c(Competition))
         """
         )
+
         args = f"decathlon, ncp={n_components}, graph=F"
+        if sample_weights:
+            robjects.r.assign("row.w", numpy2ri.py2rpy(self.sample_weights))
+            robjects.r("row.w <- as.vector(row.w)")
+            args += ", row.w=row.w"
+        if column_weights:
+            robjects.r.assign("col.w", numpy2ri.py2rpy(self.column_weights))
+            robjects.r("col.w <- as.vector(col.w)")
+            args += ", col.w=col.w"
         if not self.scale:
             args += ", scale.unit=F"
         if self.sup_cols:
@@ -94,11 +115,6 @@ def test_check_is_fitted(self):
         assert isinstance(self.pca, prince.PCA)
         sklearn.utils.validation.check_is_fitted(self.pca)
 
-    def test_svd_s(self):
-        S = self.sk_pca[-1].singular_values_
-        P = self.pca.svd_.s
-        np.testing.assert_allclose(S, P)
-
     def test_total_inertia(self):
         F = robjects.r("sum(pca$eig[,1])")[0]
         P = self.pca.total_inertia_
@@ -106,7 +122,7 @@ def test_total_inertia(self):
 
     def test_eigenvalues(self):
         P = self.pca._eigenvalues_summary
-        # Test againt FactoMineR
+        # Test against FactoMineR
         F = load_df_from_R("pca$eig")[: self.pca.n_components]
         np.testing.assert_allclose(F["eigenvalue"], P["eigenvalue"])
         np.testing.assert_allclose(F["percentage of variance"], P["% of variance"])
@@ -114,12 +130,13 @@ def test_eigenvalues(self):
             F["cumulative percentage of variance"], P["% of variance (cumulative)"]
         )
         # Test against scikit-learn
-        n = len(self.active)
-        S = self.sk_pca[-1].explained_variance_ * (n - 1) / n
-        np.testing.assert_allclose(P["eigenvalue"], S)
-        np.testing.assert_allclose(
-            P["% of variance"], self.sk_pca[-1].explained_variance_ratio_ * 100
-        )
+        if self.sample_weights is None and self.column_weights is None:
+            n = len(self.active)
+            S = self.sk_pca[-1].explained_variance_ * (n - 1) / n
+            np.testing.assert_allclose(P["eigenvalue"], S)
+            np.testing.assert_allclose(
+                P["% of variance"], self.sk_pca[-1].explained_variance_ratio_ * 100
+            )
 
     @pytest.mark.parametrize("method_name", ("row_coordinates", "transform"))
     def test_row_coords(self, method_name):
@@ -131,8 +148,9 @@ def test_row_coords(self, method_name):
             F = pd.concat((F, load_df_from_R("pca$ind.sup$coord")))
         np.testing.assert_allclose(F.abs(), P.abs())
         # Test against scikit-learn
-        S = self.sk_pca.transform(self.dataset[self.pca.feature_names_in_])
-        np.testing.assert_allclose(np.abs(S), P.abs())
+        if self.sample_weights is None and self.column_weights is None:
+            S = self.sk_pca.transform(self.dataset[self.pca.feature_names_in_])
+            np.testing.assert_allclose(np.abs(S), P.abs())
 
     def test_row_cosine_similarities(self):
         F = load_df_from_R("pca$ind$cos2")
diff --git a/tests/test_svd.py b/tests/test_svd.py
new file mode 100644
index 00000000..77105e87
--- /dev/null
+++ b/tests/test_svd.py
@@ -0,0 +1,81 @@
+from __future__ import annotations
+
+import numpy as np
+import pytest
+import rpy2.robjects as robjects
+from rpy2.robjects import numpy2ri
+
+from prince import svd
+from tests import load_df_from_R
+
+
+@pytest.mark.parametrize(
+    "n_components, are_rows_weighted, are_columns_weighted",
+    [
+        pytest.param(
+            n_components,
+            are_rows_weighted,
+            are_columns_weighted,
+            id=f"{n_components=}:{are_rows_weighted=}:{are_columns_weighted=}",
+        )
+        for n_components in [1, 3, 10]
+        for are_rows_weighted in [False, True]
+        for are_columns_weighted in [False, True]
+    ],
+)
+class TestSVD:
+    @pytest.fixture(autouse=True)
+    def _prepare(self, n_components, are_rows_weighted, are_columns_weighted):
+        self.n_components = n_components
+        self.are_rows_weighted = are_rows_weighted
+        self.are_columns_weighted = are_columns_weighted
+
+        self.dataset = np.random.rand(100, 10)
+        self.row_weights = np.random.rand(100)
+        self.row_weights /= self.row_weights.sum()
+        self.column_weights = np.random.rand(10)
+
+        # Fit Prince
+        self.svd = svd.compute_svd(
+            X=self.dataset,
+            row_weights=self.row_weights if are_rows_weighted else None,
+            column_weights=self.column_weights if are_columns_weighted else None,
+            n_components=n_components,
+            n_iter=3,
+            random_state=42,
+            engine="scipy",
+        )
+
+        # Fit FactoMineR
+        robjects.r("library('FactoMineR')")
+        robjects.r.assign("X", numpy2ri.py2rpy(self.dataset))
+        robjects.r.assign("row.w", numpy2ri.py2rpy(self.row_weights))
+        robjects.r.assign("col.w", numpy2ri.py2rpy(self.column_weights))
+        robjects.r("row.w <- as.vector(row.w)")
+        robjects.r("col.w <- as.vector(col.w)")
+        args = f"X, ncp={n_components}"
+        if are_rows_weighted:
+            args += ", row.w=row.w"
+        if are_columns_weighted:
+            args += ", col.w=col.w"
+        robjects.r(f"svd = svd.triplet({args})")
+
+    def test_U(self):
+        assert self.svd.U.shape == (100, self.n_components)
+        if self.are_rows_weighted:
+            P = self.svd.U
+            F = load_df_from_R("svd$U")
+            np.testing.assert_allclose(np.abs(F), np.abs(P))
+
+    def test_s(self):
+        assert self.svd.s.shape == (self.n_components,)
+        if self.are_rows_weighted:
+            P = self.svd.s
+            F = robjects.r("svd$vs")[: self.n_components]
+            np.testing.assert_allclose(np.abs(F), np.abs(P))
+
+    def test_V(self):
+        assert self.svd.V.shape == (self.n_components, 10)
+        P = self.svd.V
+        F = load_df_from_R("svd$V").T
+        np.testing.assert_allclose(np.abs(F), np.abs(P))