AFC_With_Python_Nobel.ipynb

{
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   "execution_count": 1,
   "id": "bada9795-0e5d-4797-94b0-945f9dbb36a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from fanalysis.ca import CA\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d46e0f1-400d-4d5b-b444-466f24eedbb2",
   "metadata": {},
   "source": [
    "# 1. Importation des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a82e65ea-5f0a-4dd0-9e71-effc20793f63",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel(r\"C:\\Users\\loris\\Desktop\\Deep_learning\\100JoursDeML-main\\100JoursDeML-main\\02_Statistiques_Pour_Le_Machine_Learning\\02_Exercice_2\\Nobel_avecMath.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "18990d04-4751-49a6-a722-bf3b8a0844eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "248203dc-4b0e-4903-8a30-5cf466c23266",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nobel_avecMath (2)</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http://www.nobelprize.org/nobel_prizes/country...</td>\n",
       "      <td>Chimie</td>\n",
       "      <td>Economie</td>\n",
       "      <td>Littérature</td>\n",
       "      <td>Médecine</td>\n",
       "      <td>Paix</td>\n",
       "      <td>Physique</td>\n",
       "      <td>Mathématiques</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Canada</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>France</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GB</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                                  Nobel_avecMath (2) Unnamed: 1 Unnamed: 2  \\\n",
       "0  http://www.nobelprize.org/nobel_prizes/country...     Chimie   Economie   \n",
       "1                                          Allemagne         24          1   \n",
       "2                                             Canada          4          3   \n",
       "3                                             France          8          3   \n",
       "4                                                 GB         23          6   \n",
       "\n",
       "    Unnamed: 3 Unnamed: 4 Unnamed: 5 Unnamed: 6     Unnamed: 7  \n",
       "0  Littérature   Médecine       Paix   Physique  Mathématiques  \n",
       "1            8         18          5         24              1  \n",
       "2            2          4          1          4              1  \n",
       "3           11         12         10          9             11  \n",
       "4            7         26         11         20              4  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ef7bd175-e5ec-4ffa-9141-4e95fd8f861b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, 8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3b2d2023-ce42-43f8-b218-5eaefa655923",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Nobel_avecMath (2)', 'Unnamed: 1', 'Unnamed: 2', 'Unnamed: 3',\n",
       "       'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7'],\n",
       "      dtype='object')"
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0f3dd298-b412-43c3-a127-91f64f26ed3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = df.iloc[0] # Remplacer les noms des colonnes par la première ligne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f7bf659b-f17d-4e7e-8587-01b5f70d104e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = df[1:] # Supprimer la première ligne, car elle est maintenant utilisée comme noms de colonnes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ba736e5f-d245-4ce0-8118-a63e58eee692",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>http://www.nobelprize.org/nobel_prizes/country-birth-map/map.php?fromyear=1901&amp;toyear=2015&amp;cat=all&amp;region=country&amp;list=yes</th>\n",
       "      <th>Chimie</th>\n",
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       "  </thead>\n",
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       "      <th>1</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
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       "      <td>24</td>\n",
       "      <td>1</td>\n",
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       "      <td>France</td>\n",
       "      <td>8</td>\n",
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       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
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       "      <th>4</th>\n",
       "      <td>GB</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Italie</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0 http://www.nobelprize.org/nobel_prizes/country-birth-map/map.php?fromyear=1901&toyear=2015&cat=all&region=country&list=yes  \\\n",
       "1                                          Allemagne                                                                           \n",
       "2                                             Canada                                                                           \n",
       "3                                             France                                                                           \n",
       "4                                                 GB                                                                           \n",
       "5                                             Italie                                                                           \n",
       "\n",
       "0 Chimie Economie Littérature Médecine Paix Physique Mathématiques  \n",
       "1     24        1           8       18    5       24             1  \n",
       "2      4        3           2        4    1        4             1  \n",
       "3      8        3          11       12   10        9            11  \n",
       "4     23        6           7       26   11       20             4  \n",
       "5      1        1           6        5    1        5             1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b05cafa6-d4d7-4024-8ad4-87e2b0cdaa7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8fac7333-db94-4d16-90be-d4ae3c2e1e4d",
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   "outputs": [
    {
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       "      <th>Chimie</th>\n",
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       "      <td>24</td>\n",
       "      <td>1</td>\n",
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       "      <td>Canada</td>\n",
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       "      <td>8</td>\n",
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       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
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       "      <th>3</th>\n",
       "      <td>GB</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Italie</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "0 http://www.nobelprize.org/nobel_prizes/country-birth-map/map.php?fromyear=1901&toyear=2015&cat=all&region=country&list=yes  \\\n",
       "0                                          Allemagne                                                                           \n",
       "1                                             Canada                                                                           \n",
       "2                                             France                                                                           \n",
       "3                                                 GB                                                                           \n",
       "4                                             Italie                                                                           \n",
       "\n",
       "0 Chimie Economie Littérature Médecine Paix Physique Mathématiques  \n",
       "0     24        1           8       18    5       24             1  \n",
       "1      4        3           2        4    1        4             1  \n",
       "2      8        3          11       12   10        9            11  \n",
       "3     23        6           7       26   11       20             4  \n",
       "4      1        1           6        5    1        5             1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "738ed231-e0c2-4e7e-a9b9-7b70f79579f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "nouveau_nom_colonne = \"Pays\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f5208cfb-8708-4338-bc62-aef38dec68a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={df.columns[0]:nouveau_nom_colonne }, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "814992ba-bb0a-4690-ba36-e3ab9a48f0ed",
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
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       "      <th>Economie</th>\n",
       "      <th>Littérature</th>\n",
       "      <th>Médecine</th>\n",
       "      <th>Paix</th>\n",
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       "      <th>Mathématiques</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Allemagne</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Canada</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>France</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GB</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Italie</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0       Pays Chimie Economie Littérature Médecine Paix Physique Mathématiques\n",
       "0  Allemagne     24        1           8       18    5       24             1\n",
       "1     Canada      4        3           2        4    1        4             1\n",
       "2     France      8        3          11       12   10        9            11\n",
       "3         GB     23        6           7       26   11       20             4\n",
       "4     Italie      1        1           6        5    1        5             1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46270d90-03d0-457b-b661-c6ec6df2a7d6",
   "metadata": {},
   "source": [
    "# 2. Description des données"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c905a44f-2f6a-42d5-9a7a-7d97b67ad3d8",
   "metadata": {},
   "source": [
    "## Distribution des Nobels des différents pays par Type de Nobel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0787a04c-a7e6-4eba-97fa-526851df2ec3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extraction des noms de colonnes et de la colonne \"Pays\"\n",
    "col_names = list(df.columns)[1:]\n",
    "Pays_names = df[\"Pays\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9e574454-6405-442d-ad1e-fdd6bc3f7328",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Chimie',\n",
       " 'Economie',\n",
       " 'Littérature',\n",
       " 'Médecine',\n",
       " 'Paix',\n",
       " 'Physique',\n",
       " 'Mathématiques']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "597d8456-1f07-4ef4-bafa-9fc321147f87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Allemagne\n",
       "1        Canada\n",
       "2        France\n",
       "3            GB\n",
       "4        Italie\n",
       "5         Japon\n",
       "6        Russie\n",
       "7           USA\n",
       "8       Afrique\n",
       "9      Amérique\n",
       "10         Asie\n",
       "11       Europe\n",
       "12      Océanie\n",
       "Name: Pays, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pays_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "787c5bbc-47bb-4f13-9b5d-5fe7ba256d16",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extraction des données numériques\n",
    "X = df[col_names].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dee78481-eafc-46a0-a94d-141f7a7ec2b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[24, 1, 8, 18, 5, 24, 1],\n",
       "       [4, 3, 2, 4, 1, 4, 1],\n",
       "       [8, 3, 11, 12, 10, 9, 11],\n",
       "       [23, 6, 7, 26, 11, 20, 4],\n",
       "       [1, 1, 6, 5, 1, 5, 1],\n",
       "       [6, 0, 2, 3, 1, 11, 3],\n",
       "       [4, 3, 5, 2, 3, 10, 9],\n",
       "       [51, 43, 8, 70, 19, 66, 13],\n",
       "       [1, 0, 5, 3, 8, 2, 1],\n",
       "       [56, 47, 18, 78, 25, 70, 15],\n",
       "       [17, 2, 10, 7, 16, 19, 7],\n",
       "       [94, 26, 79, 110, 51, 103, 34],\n",
       "       [3, 0, 0, 9, 0, 2, 2]], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "96a03ca3-5112-4bd8-b82d-f1795c7f37c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcul du profil ligne\n",
    "row_sum = np.sum(X, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "130e4301-b133-430f-acf8-824ef9c88051",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([81, 19, 64, 97, 20, 26, 36, 270, 20, 309, 78, 497, 16],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e93b8a55-5778-4e69-98c3-f4ea21559eae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[81],\n",
       "       [19],\n",
       "       [64],\n",
       "       [97],\n",
       "       [20],\n",
       "       [26],\n",
       "       [36],\n",
       "       [270],\n",
       "       [20],\n",
       "       [309],\n",
       "       [78],\n",
       "       [497],\n",
       "       [16]], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_sum[:,np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a3e4f966-693d-4aeb-a02b-af7dd31acad4",
   "metadata": {},
   "outputs": [],
   "source": [
    "row_profile = X / row_sum[:,np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0e1c0a62-e8a5-42a7-ab8c-4bf62e69c0dc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.2962962962962963, 0.012345679012345678, 0.09876543209876543,\n",
       "        0.2222222222222222, 0.06172839506172839, 0.2962962962962963,\n",
       "        0.012345679012345678],\n",
       "       [0.21052631578947367, 0.15789473684210525, 0.10526315789473684,\n",
       "        0.21052631578947367, 0.05263157894736842, 0.21052631578947367,\n",
       "        0.05263157894736842],\n",
       "       [0.125, 0.046875, 0.171875, 0.1875, 0.15625, 0.140625, 0.171875],\n",
       "       [0.23711340206185566, 0.061855670103092786, 0.07216494845360824,\n",
       "        0.26804123711340205, 0.1134020618556701, 0.20618556701030927,\n",
       "        0.041237113402061855],\n",
       "       [0.05, 0.05, 0.3, 0.25, 0.05, 0.25, 0.05],\n",
       "       [0.23076923076923078, 0.0, 0.07692307692307693,\n",
       "        0.11538461538461539, 0.038461538461538464, 0.4230769230769231,\n",
       "        0.11538461538461539],\n",
       "       [0.1111111111111111, 0.08333333333333333, 0.1388888888888889,\n",
       "        0.05555555555555555, 0.08333333333333333, 0.2777777777777778,\n",
       "        0.25],\n",
       "       [0.18888888888888888, 0.15925925925925927, 0.02962962962962963,\n",
       "        0.25925925925925924, 0.07037037037037037, 0.24444444444444444,\n",
       "        0.04814814814814815],\n",
       "       [0.05, 0.0, 0.25, 0.15, 0.4, 0.1, 0.05],\n",
       "       [0.18122977346278318, 0.15210355987055016, 0.05825242718446602,\n",
       "        0.2524271844660194, 0.08090614886731391, 0.22653721682847897,\n",
       "        0.04854368932038835],\n",
       "       [0.21794871794871795, 0.02564102564102564, 0.1282051282051282,\n",
       "        0.08974358974358974, 0.20512820512820512, 0.24358974358974358,\n",
       "        0.08974358974358974],\n",
       "       [0.1891348088531187, 0.052313883299798795, 0.158953722334004,\n",
       "        0.22132796780684105, 0.10261569416498995, 0.20724346076458752,\n",
       "        0.06841046277665996],\n",
       "       [0.1875, 0.0, 0.0, 0.5625, 0.0, 0.125, 0.125]], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d47855df-c701-46a1-bd14-cba44d148533",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Profil ligne:\n",
      "             Chimie  Economie Littérature  Médecine      Paix  Physique  \\\n",
      "Pays                                                                      \n",
      "Allemagne  0.296296  0.012346    0.098765  0.222222  0.061728  0.296296   \n",
      "Canada     0.210526  0.157895    0.105263  0.210526  0.052632  0.210526   \n",
      "France        0.125  0.046875    0.171875    0.1875   0.15625  0.140625   \n",
      "GB         0.237113  0.061856    0.072165  0.268041  0.113402  0.206186   \n",
      "Italie         0.05      0.05         0.3      0.25      0.05      0.25   \n",
      "Japon      0.230769       0.0    0.076923  0.115385  0.038462  0.423077   \n",
      "Russie     0.111111  0.083333    0.138889  0.055556  0.083333  0.277778   \n",
      "USA        0.188889  0.159259     0.02963  0.259259   0.07037  0.244444   \n",
      "Afrique        0.05       0.0        0.25      0.15       0.4       0.1   \n",
      "Amérique    0.18123  0.152104    0.058252  0.252427  0.080906  0.226537   \n",
      "Asie       0.217949  0.025641    0.128205  0.089744  0.205128   0.24359   \n",
      "Europe     0.189135  0.052314    0.158954  0.221328  0.102616  0.207243   \n",
      "Océanie      0.1875       0.0         0.0    0.5625       0.0     0.125   \n",
      "\n",
      "          Mathématiques  \n",
      "Pays                     \n",
      "Allemagne      0.012346  \n",
      "Canada         0.052632  \n",
      "France         0.171875  \n",
      "GB             0.041237  \n",
      "Italie             0.05  \n",
      "Japon          0.115385  \n",
      "Russie             0.25  \n",
      "USA            0.048148  \n",
      "Afrique            0.05  \n",
      "Amérique       0.048544  \n",
      "Asie           0.089744  \n",
      "Europe          0.06841  \n",
      "Océanie           0.125  \n"
     ]
    }
   ],
   "source": [
    "# Affichage\n",
    "print(\"Profil ligne:\")\n",
    "print(pd.DataFrame(row_profile, index=Pays_names, columns=col_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52127777-d990-4303-a08d-e1d03fee8ae9",
   "metadata": {},
   "source": [
    "## Répartition des différents types Nobels obtenus selon les Pays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5849ff69-2cc1-4663-9132-cfadcc2538c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Profil colonne:\n",
      "             Chimie  Economie Littérature  Médecine      Paix  Physique  \\\n",
      "Pays                                                                      \n",
      "Allemagne  0.082192  0.007407    0.049689  0.051873  0.033113  0.069565   \n",
      "Canada     0.013699  0.022222    0.012422  0.011527  0.006623  0.011594   \n",
      "France     0.027397  0.022222    0.068323  0.034582  0.066225  0.026087   \n",
      "GB         0.078767  0.044444    0.043478  0.074928  0.072848  0.057971   \n",
      "Italie     0.003425  0.007407    0.037267  0.014409  0.006623  0.014493   \n",
      "Japon      0.020548       0.0    0.012422  0.008646  0.006623  0.031884   \n",
      "Russie     0.013699  0.022222    0.031056  0.005764  0.019868  0.028986   \n",
      "USA        0.174658  0.318519    0.049689  0.201729  0.125828  0.191304   \n",
      "Afrique    0.003425       0.0    0.031056  0.008646   0.05298  0.005797   \n",
      "Amérique   0.191781  0.348148    0.111801  0.224784  0.165563  0.202899   \n",
      "Asie       0.058219  0.014815    0.062112  0.020173   0.10596  0.055072   \n",
      "Europe     0.321918  0.192593    0.490683  0.317003  0.337748  0.298551   \n",
      "Océanie    0.010274       0.0         0.0  0.025937       0.0  0.005797   \n",
      "\n",
      "          Mathématiques  \n",
      "Pays                     \n",
      "Allemagne      0.009804  \n",
      "Canada         0.009804  \n",
      "France         0.107843  \n",
      "GB             0.039216  \n",
      "Italie         0.009804  \n",
      "Japon          0.029412  \n",
      "Russie         0.088235  \n",
      "USA            0.127451  \n",
      "Afrique        0.009804  \n",
      "Amérique       0.147059  \n",
      "Asie           0.068627  \n",
      "Europe         0.333333  \n",
      "Océanie        0.019608  \n"
     ]
    }
   ],
   "source": [
    "# Calcul du profil colonne\n",
    "col_sum = np.sum(X, axis=0)\n",
    "col_profile = X / col_sum[np.newaxis, :]\n",
    "\n",
    "# Affichage\n",
    "print(\"Profil colonne:\")\n",
    "print(pd.DataFrame(col_profile, index=Pays_names, columns=col_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a980717e-080c-4c4c-9470-c215799e1c1e",
   "metadata": {},
   "source": [
    "## Analyse des Nobels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "692a8023-fc49-4cf9-98ff-59313cdcb5d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extraction des noms de colonnes\n",
    "col_names = list(df.columns)[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cbd0b245-cd1e-4be3-87de-729acec754dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0\n",
       "Chimie           292\n",
       "Economie         135\n",
       "Littérature      161\n",
       "Médecine         347\n",
       "Paix             151\n",
       "Physique         345\n",
       "Mathématiques    102\n",
       "dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcul de la somme des Nobels pour chaque type de Nobel \n",
    "total_nobels = df[col_names].sum()\n",
    "total_nobels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "25a4a935-c54e-46bc-b96a-73ddfd2eb5de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0\n",
       "Chimie           0.190476\n",
       "Economie         0.088063\n",
       "Littérature      0.105023\n",
       "Médecine         0.226354\n",
       "Paix               0.0985\n",
       "Physique         0.225049\n",
       "Mathématiques    0.066536\n",
       "dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcul des proportions pour chaque candidat\n",
    "prop_nobels = total_nobels / total_nobels.sum()\n",
    "prop_nobels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "863685b1-1d60-4d0c-bdd8-011f1a9cf4a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0\n",
       "Médecine         0.226354\n",
       "Physique         0.225049\n",
       "Chimie           0.190476\n",
       "Littérature      0.105023\n",
       "Paix               0.0985\n",
       "Economie         0.088063\n",
       "Mathématiques    0.066536\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tri des proportions de la plus grande à la plus petite\n",
    "prop_nobels = prop_nobels.sort_values(ascending=False)\n",
    "prop_nobels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "077f3802-f875-415b-a0d6-13d9e38f737e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Création du diagramme en barres \n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax.bar(prop_nobels.index, prop_nobels)\n",
    "\n",
    "# Ajout de labels et de titres\n",
    "ax.set_xlabel(\"Types de Nobels\")\n",
    "ax.set_ylabel(\"% Proportion \")\n",
    "ax.set_title(\"Répartition des Nobels par Types\")\n",
    "\n",
    "# Rotation des noms des candidats\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "\n",
    "# Affichage\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dce9778-6583-4e7a-8aa6-e1eaefa26444",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# 3. L'Analyse Factorielle des Correspondances (AFC) - Théorie\n",
    "L'analyse factorielle des correspondances (AFC) est une méthode statistique qui permet de réduire les dimensions d'un tableau de données croisées. Elle permet également de visualiser la structure des relations entre les variables et les individus en utilisant des graphiques."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "156ac8e7-dadd-49d5-bf1b-e8ce078e5f2b",
   "metadata": {},
   "source": [
    "\n",
    "## 3.1. Étape 1 : Construction du tableau de données\n",
    "L'AFC nécessite un tableau de données croisées appelé \"tableau de contingence\". Ce tableau doit être construit à partir des données brutes, en croisant les valeurs de chaque variable.\n",
    "\n",
    "Variable 1\tVariable 2\t...\tVariable p\n",
    "Individu 1\t\n",
    "...\t\n",
    "Individu 2\t\n",
    "...\t\n",
    "...\t...\t...\t...\t...\n",
    "Individu n\t\n",
    "...\t\n",
    "Chaque \n",
    " représente le nombre d'individus ayant à la fois la valeur \n",
    " pour la variable 1 et la valeur \n",
    " pour la variable 2.\n",
    "\n",
    "## 3.2. Étape 2 : Calcul de la distance de khi-deux\n",
    "La distance de khi-deux est une mesure de la différence entre les observations et les fréquences attendues sous l'hypothèse d'indépendance entre les variables. Elle est utilisée pour mesurer la similitude entre deux variables. La formule de la distance de khi-deux est la suivante :\n",
    "\n",
    " \n",
    "\n",
    "où \n",
    " est la somme des effectifs de la ligne \n",
    ", \n",
    " est la somme des effectifs de la colonne \n",
    " et \n",
    " est la somme totale des effectifs du tableau.\n",
    "\n",
    "## 3.3. Étape 3 : Calcul de la matrice des distances\n",
    "La matrice des distances est une matrice carrée qui mesure les distances entre chaque paire de variables. Elle est calculée en utilisant la distance de khi-deux entre chaque paire de variables.\n",
    "\n",
    "## 3.4. Étape 4 : Calcul des cosinus carrés et des contributions\n",
    "Les cosinus carrés mesurent l'association entre chaque variable et chaque axe factoriel. Ils sont calculés en utilisant la formule suivante :\n",
    "\n",
    " \n",
    "\n",
    "où \n",
    " est la fréquence observée de la variable \n",
    " pour la catégorie \n",
    ", \n",
    " est la somme des fréquences de la variable \n",
    " et \n",
    " est la somme des fréquences de la catégorie \n",
    ".\n",
    "\n",
    "Les contributions mesurent l'importance de chaque variable pour l'axe factoriel correspondant. Elles sont calculées en utilisant la formule suivante :\n",
    "\n",
    " \n",
    "\n",
    "où \n",
    " est la valeur propre de l'axe factoriel \n",
    ". Les contributions sont exprimées en pourcentage et indiquent la part de la variance expliquée par chaque variable sur l'axe factoriel correspondant.\n",
    "\n",
    "Les cosinus carrés et les contributions sont des indicateurs importants pour interpréter les résultats de l'AFC et sélectionner les variables les plus pertinentes pour chaque axe factoriel.\n",
    "\n",
    "## 3.5. Étape 5 : Calcul des valeurs propres\n",
    "Les valeurs propres sont des mesures de l'importance de chaque axe factoriel pour expliquer la variation totale des données.\n",
    "\n",
    "Les valeurs propres sont calculées à partir des cosinus carrés des variables sur chaque axe factoriel. La formule de calcul est la suivante :\n",
    "\n",
    "\n",
    "où \n",
    " est la valeur propre de l'axe factoriel \n",
    " et \n",
    " est le carré du cosinus de l'angle formé entre la variable \n",
    " et l'axe factoriel \n",
    ". Les valeurs propres sont ordonnées de manière décroissante, et l'axe factoriel qui a la plus grande valeur propre est appelé le premier axe factoriel.\n",
    "\n",
    "## 3.6. Étape 6 : Construction du graphique\n",
    "Le graphique de l'AFC est un nuage de points qui représente les individus et les variables dans un espace de dimension réduite. Les axes du graphique sont les axes factoriels, et les points sont placés en fonction de leurs coordonnées sur ces axes. Les points qui sont proches les uns des autres ont des profils similaires.\n",
    "\n",
    "Pour construire le graphique de l'AFC, on utilise les coordonnées factorielles des individus et des variables qui ont été calculées à l'étape 4. Les individus sont représentés par des cercles, et les variables sont représentées par des flèches qui indiquent leur contribution à chaque axe factoriel.\n",
    "\n",
    "On peut également colorer les cercles ou les flèches en fonction d'une variable supplémentaire pour visualiser la relation entre cette variable et les axes factoriels.\n",
    "\n",
    "## 3.7. Résumé\n",
    "L'Analyse Factorielle des Correspondances (AFC) est une méthode statistique qui permet de réduire les dimensions d'un tableau de données croisées. Elle permet également de visualiser la structure des relations entre les variables et les individus en utilisant des graphiques."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c99b7ff5-b337-48d3-a5b8-019c5da6a5a1",
   "metadata": {},
   "source": [
    "# 4. AFC pratique"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97f488a6-08cc-4c4f-8300-4d00b78fe040",
   "metadata": {},
   "source": [
    "## 4.1. Application de l'AFC avec fanalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1eddb17e-5136-4d15-a758-04fee710c7b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.set_index(\"Pays\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ad041ce8-f3b2-40bc-b6e5-4737501c5f43",
   "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>Chimie</th>\n",
       "      <th>Economie</th>\n",
       "      <th>Littérature</th>\n",
       "      <th>Médecine</th>\n",
       "      <th>Paix</th>\n",
       "      <th>Physique</th>\n",
       "      <th>Mathématiques</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pays</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>Allemagne</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GB</th>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italie</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japon</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russie</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>51</td>\n",
       "      <td>43</td>\n",
       "      <td>8</td>\n",
       "      <td>70</td>\n",
       "      <td>19</td>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afrique</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amérique</th>\n",
       "      <td>56</td>\n",
       "      <td>47</td>\n",
       "      <td>18</td>\n",
       "      <td>78</td>\n",
       "      <td>25</td>\n",
       "      <td>70</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asie</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>94</td>\n",
       "      <td>26</td>\n",
       "      <td>79</td>\n",
       "      <td>110</td>\n",
       "      <td>51</td>\n",
       "      <td>103</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Océanie</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0         Chimie Economie Littérature Médecine Paix Physique Mathématiques\n",
       "Pays                                                                      \n",
       "Allemagne     24        1           8       18    5       24             1\n",
       "Canada         4        3           2        4    1        4             1\n",
       "France         8        3          11       12   10        9            11\n",
       "GB            23        6           7       26   11       20             4\n",
       "Italie         1        1           6        5    1        5             1\n",
       "Japon          6        0           2        3    1       11             3\n",
       "Russie         4        3           5        2    3       10             9\n",
       "USA           51       43           8       70   19       66            13\n",
       "Afrique        1        0           5        3    8        2             1\n",
       "Amérique      56       47          18       78   25       70            15\n",
       "Asie          17        2          10        7   16       19             7\n",
       "Europe        94       26          79      110   51      103            34\n",
       "Océanie        3        0           0        9    0        2             2"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "00632425-9a4a-4682-ac52-e766b838e2ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# On crée une instale de la classe CA en passant les étiquettes de lignes et les colonnes\n",
    "my_ca=CA(row_labels=df.index.values,col_labels=df.columns.values,stats=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "19b6319d-df09-42b4-9609-da422118b3a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Allemagne', 'Canada', 'France', 'GB', 'Italie', 'Japon', 'Russie',\n",
       "       'USA', 'Afrique', 'Amérique', 'Asie', 'Europe', 'Océanie'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_labels=df.index.values\n",
    "row_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a75a26c4-48b9-4027-9d57-061e61f15dee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Chimie', 'Economie', 'Littérature', 'Médecine', 'Paix',\n",
       "       'Physique', 'Mathématiques'], dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_labels=df.columns.values\n",
    "col_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9227b3db-5420-458c-b3c4-e559ae4d6540",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[24, 1, 8, 18, 5, 24, 1],\n",
       "       [4, 3, 2, 4, 1, 4, 1],\n",
       "       [8, 3, 11, 12, 10, 9, 11],\n",
       "       [23, 6, 7, 26, 11, 20, 4],\n",
       "       [1, 1, 6, 5, 1, 5, 1],\n",
       "       [6, 0, 2, 3, 1, 11, 3],\n",
       "       [4, 3, 5, 2, 3, 10, 9],\n",
       "       [51, 43, 8, 70, 19, 66, 13],\n",
       "       [1, 0, 5, 3, 8, 2, 1],\n",
       "       [56, 47, 18, 78, 25, 70, 15],\n",
       "       [17, 2, 10, 7, 16, 19, 7],\n",
       "       [94, 26, 79, 110, 51, 103, 34],\n",
       "       [3, 0, 0, 9, 0, 2, 2]], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.values\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c2c1f62c-d255-4426-a2c5-41b3943d44e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtype = x.dtype\n",
    "dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "923565dc-c3be-41fb-8b26-83ff85e34383",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = x.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "dc498c12-c793-4df1-b463-ef5c4409eb07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 24.,   1.,   8.,  18.,   5.,  24.,   1.],\n",
       "       [  4.,   3.,   2.,   4.,   1.,   4.,   1.],\n",
       "       [  8.,   3.,  11.,  12.,  10.,   9.,  11.],\n",
       "       [ 23.,   6.,   7.,  26.,  11.,  20.,   4.],\n",
       "       [  1.,   1.,   6.,   5.,   1.,   5.,   1.],\n",
       "       [  6.,   0.,   2.,   3.,   1.,  11.,   3.],\n",
       "       [  4.,   3.,   5.,   2.,   3.,  10.,   9.],\n",
       "       [ 51.,  43.,   8.,  70.,  19.,  66.,  13.],\n",
       "       [  1.,   0.,   5.,   3.,   8.,   2.,   1.],\n",
       "       [ 56.,  47.,  18.,  78.,  25.,  70.,  15.],\n",
       "       [ 17.,   2.,  10.,   7.,  16.,  19.,   7.],\n",
       "       [ 94.,  26.,  79., 110.,  51., 103.,  34.],\n",
       "       [  3.,   0.,   0.,   9.,   0.,   2.,   2.]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9b7b9663-c9f2-4b0d-bd72-7ecd07d345fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtype = x.dtype\n",
    "dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "755d7472-324c-4bb3-a91c-05c838dd0892",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>CA(col_labels=array([&#x27;Chimie&#x27;, &#x27;Economie&#x27;, &#x27;Littérature&#x27;, &#x27;Médecine&#x27;, &#x27;Paix&#x27;,\n",
       "       &#x27;Physique&#x27;, &#x27;Mathématiques&#x27;], dtype=object),\n",
       "   row_labels=array([&#x27;Allemagne&#x27;, &#x27;Canada&#x27;, &#x27;France&#x27;, &#x27;GB&#x27;, &#x27;Italie&#x27;, &#x27;Japon&#x27;, &#x27;Russie&#x27;,\n",
       "       &#x27;USA&#x27;, &#x27;Afrique&#x27;, &#x27;Amérique&#x27;, &#x27;Asie&#x27;, &#x27;Europe&#x27;, &#x27;Océanie&#x27;],\n",
       "      dtype=object))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;CA<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>CA(col_labels=array([&#x27;Chimie&#x27;, &#x27;Economie&#x27;, &#x27;Littérature&#x27;, &#x27;Médecine&#x27;, &#x27;Paix&#x27;,\n",
       "       &#x27;Physique&#x27;, &#x27;Mathématiques&#x27;], dtype=object),\n",
       "   row_labels=array([&#x27;Allemagne&#x27;, &#x27;Canada&#x27;, &#x27;France&#x27;, &#x27;GB&#x27;, &#x27;Italie&#x27;, &#x27;Japon&#x27;, &#x27;Russie&#x27;,\n",
       "       &#x27;USA&#x27;, &#x27;Afrique&#x27;, &#x27;Amérique&#x27;, &#x27;Asie&#x27;, &#x27;Europe&#x27;, &#x27;Océanie&#x27;],\n",
       "      dtype=object))</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "CA(col_labels=array(['Chimie', 'Economie', 'Littérature', 'Médecine', 'Paix',\n",
       "       'Physique', 'Mathématiques'], dtype=object),\n",
       "   row_labels=array(['Allemagne', 'Canada', 'France', 'GB', 'Italie', 'Japon', 'Russie',\n",
       "       'USA', 'Afrique', 'Amérique', 'Asie', 'Europe', 'Océanie'],\n",
       "      dtype=object))"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Estime l'AFC\n",
    "my_ca.fit(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18e05520-a9d1-4e54-837f-041ffc3c4006",
   "metadata": {},
   "source": [
    "## 4.2. Aides à l'interprétation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce040fb7-1785-4b7d-8ab3-fba13bc4e130",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### 4.2.1. Choix du nombre d'axe factoriel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "18d75eda-9167-472d-aefa-6d47e7b65a3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.88919627e-02, 2.64897049e-02, 2.01714398e-02, 1.55598136e-02,\n",
       "        9.68700954e-03, 1.88503546e-03],\n",
       "       [5.16697647e+01, 1.73492555e+01, 1.32111499e+01, 1.01907961e+01,\n",
       "        6.34444228e+00, 1.23459140e+00],\n",
       "       [5.16697647e+01, 6.90190203e+01, 8.22301702e+01, 9.24209663e+01,\n",
       "        9.87654086e+01, 1.00000000e+02]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identification des valeurs propres\n",
    "my_ca.eig_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fafc0878-4155-4272-9fcb-0b27d974fa67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graphique des valeurs propres\n",
    "my_ca.plot_eigenvalues()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f369bcc3-0558-43ca-8293-1ec0e3317674",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_ca.plot_eigenvalues(type=\"percentage\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "09e820e0-866d-4a23-b52f-6fad480e0a21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_ca.plot_eigenvalues(type=\"cumulative\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0364bbef-5445-45b3-82dc-dcb95b80345e",
   "metadata": {},
   "source": [
    "### 4.2.2. Analyse des qualités de représentation (cos 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36ac4e1b-dc99-4927-9913-a2af7ddfab45",
   "metadata": {},
   "source": [
    "#### 4.2.2.1. Analyse suivant le profil ligne (les Pays)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "869b1d98-17b8-45a8-9269-e9e669fef2b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Premier axe factoriel\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Second axe factoriel\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Classement des points colonnes en fonction de leur contribution au 1er axe\n",
    "print(\"Premier axe factoriel\")\n",
    "my_ca.plot_row_cos2(num_axis=1)\n",
    "print(\"Second axe factoriel\")\n",
    "my_ca.plot_row_cos2(num_axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b7832512-2703-4048-bdb0-ca7405d1b453",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Premier axe factoriel\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Second axe factoriel\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### Profil colonne (les types de Nobels)\n",
    "\n",
    "print(\"Premier axe factoriel\")\n",
    "my_ca.plot_col_cos2(num_axis=1)\n",
    "print(\"Second axe factoriel\")\n",
    "my_ca.plot_col_cos2(num_axis=2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ae6dda7-0783-463e-a45e-ab156c883573",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### 4.2.3. Analyses des contributions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f39fd3a8-bae5-4b4a-bfc9-e342bbfe591d",
   "metadata": {},
   "source": [
    "#### 4.2.3.1. Profil lignes (Pays)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "73eda17c-ca19-487e-84da-bbb67600e2ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Premier axe\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Second axe\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analyse  du premier plan factoriel - les Pays\n",
    "print(\"Premier axe\")\n",
    "my_ca.plot_row_contrib(num_axis=1)\n",
    "print(\"Second axe\")\n",
    "my_ca.plot_row_contrib(num_axis=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd84b38d-9540-4901-8568-170cd8541a29",
   "metadata": {},
   "source": [
    "#### 4.2.3.2. Profil colonne (Types de Nobels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9e935779-245e-41de-bb3b-baa113074b3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Premier axe\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Second axe\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analyse  du premier plan factoriel - les types de Nobels\n",
    "print(\"Premier axe\")\n",
    "my_ca.plot_col_contrib(num_axis=1)\n",
    "print(\"Second axe\")\n",
    "my_ca.plot_col_contrib(num_axis=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe4f42ed-eb4c-4c22-b508-5670e1c18de0",
   "metadata": {},
   "source": [
    "### 4.2.4. Tableau global"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "632d13c1-b618-4867-927d-a0190ad03844",
   "metadata": {},
   "source": [
    "Coordonnées, contribution et les cosinus carré des départements sur les axes factoriels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a979456b-b550-4f02-ab28-5b42af1dc038",
   "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>row_coord_dim1</th>\n",
       "      <th>row_coord_dim2</th>\n",
       "      <th>row_coord_dim3</th>\n",
       "      <th>row_coord_dim4</th>\n",
       "      <th>row_coord_dim5</th>\n",
       "      <th>row_coord_dim6</th>\n",
       "      <th>row_contrib_dim1</th>\n",
       "      <th>row_contrib_dim2</th>\n",
       "      <th>row_contrib_dim3</th>\n",
       "      <th>row_contrib_dim4</th>\n",
       "      <th>row_contrib_dim5</th>\n",
       "      <th>row_contrib_dim6</th>\n",
       "      <th>row_cos2_dim1</th>\n",
       "      <th>row_cos2_dim2</th>\n",
       "      <th>row_cos2_dim3</th>\n",
       "      <th>row_cos2_dim4</th>\n",
       "      <th>row_cos2_dim5</th>\n",
       "      <th>row_cos2_dim6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Allemagne</th>\n",
       "      <td>-0.011752</td>\n",
       "      <td>-0.397745</td>\n",
       "      <td>-0.175968</td>\n",
       "      <td>0.124044</td>\n",
       "      <td>-0.004118</td>\n",
       "      <td>0.007103</td>\n",
       "      <td>0.009250</td>\n",
       "      <td>31.555446</td>\n",
       "      <td>8.110961</td>\n",
       "      <td>5.225067</td>\n",
       "      <td>0.009248</td>\n",
       "      <td>0.141412</td>\n",
       "      <td>0.000675</td>\n",
       "      <td>0.772623</td>\n",
       "      <td>0.151226</td>\n",
       "      <td>0.075147</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>2.463903e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>-0.194837</td>\n",
       "      <td>0.061139</td>\n",
       "      <td>0.001792</td>\n",
       "      <td>-0.032315</td>\n",
       "      <td>-0.153017</td>\n",
       "      <td>0.132794</td>\n",
       "      <td>0.596381</td>\n",
       "      <td>0.174894</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>0.083178</td>\n",
       "      <td>2.995717</td>\n",
       "      <td>11.594474</td>\n",
       "      <td>0.453026</td>\n",
       "      <td>0.044609</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.012462</td>\n",
       "      <td>0.279420</td>\n",
       "      <td>2.104446e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>0.447115</td>\n",
       "      <td>0.300072</td>\n",
       "      <td>-0.006457</td>\n",
       "      <td>-0.117527</td>\n",
       "      <td>0.133748</td>\n",
       "      <td>0.039959</td>\n",
       "      <td>10.578960</td>\n",
       "      <td>14.191001</td>\n",
       "      <td>0.008629</td>\n",
       "      <td>3.706053</td>\n",
       "      <td>7.709494</td>\n",
       "      <td>3.536354</td>\n",
       "      <td>0.618357</td>\n",
       "      <td>0.278518</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>0.042725</td>\n",
       "      <td>0.055332</td>\n",
       "      <td>4.938996e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GB</th>\n",
       "      <td>-0.053303</td>\n",
       "      <td>-0.157111</td>\n",
       "      <td>0.047093</td>\n",
       "      <td>0.034919</td>\n",
       "      <td>0.139203</td>\n",
       "      <td>0.017069</td>\n",
       "      <td>0.227880</td>\n",
       "      <td>5.896092</td>\n",
       "      <td>0.695668</td>\n",
       "      <td>0.495848</td>\n",
       "      <td>12.657155</td>\n",
       "      <td>0.977962</td>\n",
       "      <td>0.056117</td>\n",
       "      <td>0.487524</td>\n",
       "      <td>0.043802</td>\n",
       "      <td>0.024083</td>\n",
       "      <td>0.382720</td>\n",
       "      <td>5.754353e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italie</th>\n",
       "      <td>0.364286</td>\n",
       "      <td>-0.084544</td>\n",
       "      <td>-0.016453</td>\n",
       "      <td>-0.408109</td>\n",
       "      <td>-0.421700</td>\n",
       "      <td>-0.175363</td>\n",
       "      <td>2.194521</td>\n",
       "      <td>0.352027</td>\n",
       "      <td>0.017509</td>\n",
       "      <td>13.964833</td>\n",
       "      <td>23.950026</td>\n",
       "      <td>21.283525</td>\n",
       "      <td>0.257549</td>\n",
       "      <td>0.013872</td>\n",
       "      <td>0.000525</td>\n",
       "      <td>0.323241</td>\n",
       "      <td>0.345130</td>\n",
       "      <td>5.968288e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japon</th>\n",
       "      <td>0.113482</td>\n",
       "      <td>-0.053815</td>\n",
       "      <td>-0.537568</td>\n",
       "      <td>0.270186</td>\n",
       "      <td>-0.025567</td>\n",
       "      <td>-0.163730</td>\n",
       "      <td>0.276856</td>\n",
       "      <td>0.185419</td>\n",
       "      <td>24.297470</td>\n",
       "      <td>7.957081</td>\n",
       "      <td>0.114450</td>\n",
       "      <td>24.119537</td>\n",
       "      <td>0.031781</td>\n",
       "      <td>0.007147</td>\n",
       "      <td>0.713150</td>\n",
       "      <td>0.180153</td>\n",
       "      <td>0.001613</td>\n",
       "      <td>6.615631e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russie</th>\n",
       "      <td>0.372250</td>\n",
       "      <td>0.612342</td>\n",
       "      <td>-0.418718</td>\n",
       "      <td>0.065899</td>\n",
       "      <td>-0.022005</td>\n",
       "      <td>-0.001218</td>\n",
       "      <td>4.124734</td>\n",
       "      <td>33.240778</td>\n",
       "      <td>20.411086</td>\n",
       "      <td>0.655407</td>\n",
       "      <td>0.117380</td>\n",
       "      <td>0.001847</td>\n",
       "      <td>0.199759</td>\n",
       "      <td>0.540537</td>\n",
       "      <td>0.252744</td>\n",
       "      <td>0.006260</td>\n",
       "      <td>0.000698</td>\n",
       "      <td>2.137221e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>-0.351308</td>\n",
       "      <td>0.075114</td>\n",
       "      <td>0.027614</td>\n",
       "      <td>0.033452</td>\n",
       "      <td>0.000694</td>\n",
       "      <td>-0.018039</td>\n",
       "      <td>27.552774</td>\n",
       "      <td>3.751355</td>\n",
       "      <td>0.665785</td>\n",
       "      <td>1.266654</td>\n",
       "      <td>0.000877</td>\n",
       "      <td>3.040235</td>\n",
       "      <td>0.940202</td>\n",
       "      <td>0.042982</td>\n",
       "      <td>0.005809</td>\n",
       "      <td>0.008525</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>2.478846e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afrique</th>\n",
       "      <td>0.881854</td>\n",
       "      <td>0.047991</td>\n",
       "      <td>0.760782</td>\n",
       "      <td>0.134307</td>\n",
       "      <td>0.084513</td>\n",
       "      <td>-0.173419</td>\n",
       "      <td>12.860236</td>\n",
       "      <td>0.113432</td>\n",
       "      <td>37.434437</td>\n",
       "      <td>1.512458</td>\n",
       "      <td>0.961939</td>\n",
       "      <td>20.814390</td>\n",
       "      <td>0.549971</td>\n",
       "      <td>0.001629</td>\n",
       "      <td>0.409323</td>\n",
       "      <td>0.012757</td>\n",
       "      <td>0.005051</td>\n",
       "      <td>2.126872e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amérique</th>\n",
       "      <td>-0.262475</td>\n",
       "      <td>0.070735</td>\n",
       "      <td>0.064958</td>\n",
       "      <td>0.001382</td>\n",
       "      <td>-0.027427</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>17.601936</td>\n",
       "      <td>3.807261</td>\n",
       "      <td>4.216432</td>\n",
       "      <td>0.002475</td>\n",
       "      <td>1.565234</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.873499</td>\n",
       "      <td>0.063439</td>\n",
       "      <td>0.053500</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.009538</td>\n",
       "      <td>8.206865e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asie</th>\n",
       "      <td>0.387558</td>\n",
       "      <td>0.021135</td>\n",
       "      <td>0.054028</td>\n",
       "      <td>0.321344</td>\n",
       "      <td>0.062386</td>\n",
       "      <td>0.010166</td>\n",
       "      <td>9.687069</td>\n",
       "      <td>0.085802</td>\n",
       "      <td>0.736303</td>\n",
       "      <td>33.766612</td>\n",
       "      <td>2.044239</td>\n",
       "      <td>0.278968</td>\n",
       "      <td>0.575871</td>\n",
       "      <td>0.001713</td>\n",
       "      <td>0.011192</td>\n",
       "      <td>0.395906</td>\n",
       "      <td>0.014922</td>\n",
       "      <td>3.962540e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>0.180654</td>\n",
       "      <td>-0.069036</td>\n",
       "      <td>-0.006953</td>\n",
       "      <td>-0.069928</td>\n",
       "      <td>-0.035006</td>\n",
       "      <td>0.019890</td>\n",
       "      <td>13.411446</td>\n",
       "      <td>5.832902</td>\n",
       "      <td>0.077698</td>\n",
       "      <td>10.188522</td>\n",
       "      <td>4.101223</td>\n",
       "      <td>6.803822</td>\n",
       "      <td>0.742380</td>\n",
       "      <td>0.108413</td>\n",
       "      <td>0.001100</td>\n",
       "      <td>0.111233</td>\n",
       "      <td>0.027875</td>\n",
       "      <td>8.998924e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Océanie</th>\n",
       "      <td>-0.257611</td>\n",
       "      <td>-0.143699</td>\n",
       "      <td>-0.253606</td>\n",
       "      <td>-0.561867</td>\n",
       "      <td>0.637396</td>\n",
       "      <td>-0.115666</td>\n",
       "      <td>0.877958</td>\n",
       "      <td>0.813593</td>\n",
       "      <td>3.327824</td>\n",
       "      <td>21.175813</td>\n",
       "      <td>43.773019</td>\n",
       "      <td>7.407467</td>\n",
       "      <td>0.074845</td>\n",
       "      <td>0.023289</td>\n",
       "      <td>0.072536</td>\n",
       "      <td>0.356043</td>\n",
       "      <td>0.458199</td>\n",
       "      <td>1.508853e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           row_coord_dim1  row_coord_dim2  row_coord_dim3  row_coord_dim4  \\\n",
       "Allemagne       -0.011752       -0.397745       -0.175968        0.124044   \n",
       "Canada          -0.194837        0.061139        0.001792       -0.032315   \n",
       "France           0.447115        0.300072       -0.006457       -0.117527   \n",
       "GB              -0.053303       -0.157111        0.047093        0.034919   \n",
       "Italie           0.364286       -0.084544       -0.016453       -0.408109   \n",
       "Japon            0.113482       -0.053815       -0.537568        0.270186   \n",
       "Russie           0.372250        0.612342       -0.418718        0.065899   \n",
       "USA             -0.351308        0.075114        0.027614        0.033452   \n",
       "Afrique          0.881854        0.047991        0.760782        0.134307   \n",
       "Amérique        -0.262475        0.070735        0.064958        0.001382   \n",
       "Asie             0.387558        0.021135        0.054028        0.321344   \n",
       "Europe           0.180654       -0.069036       -0.006953       -0.069928   \n",
       "Océanie         -0.257611       -0.143699       -0.253606       -0.561867   \n",
       "\n",
       "           row_coord_dim5  row_coord_dim6  row_contrib_dim1  row_contrib_dim2  \\\n",
       "Allemagne       -0.004118        0.007103          0.009250         31.555446   \n",
       "Canada          -0.153017        0.132794          0.596381          0.174894   \n",
       "France           0.133748        0.039959         10.578960         14.191001   \n",
       "GB               0.139203        0.017069          0.227880          5.896092   \n",
       "Italie          -0.421700       -0.175363          2.194521          0.352027   \n",
       "Japon           -0.025567       -0.163730          0.276856          0.185419   \n",
       "Russie          -0.022005       -0.001218          4.124734         33.240778   \n",
       "USA              0.000694       -0.018039         27.552774          3.751355   \n",
       "Afrique          0.084513       -0.173419         12.860236          0.113432   \n",
       "Amérique        -0.027427        0.000025         17.601936          3.807261   \n",
       "Asie             0.062386        0.010166          9.687069          0.085802   \n",
       "Europe          -0.035006        0.019890         13.411446          5.832902   \n",
       "Océanie          0.637396       -0.115666          0.877958          0.813593   \n",
       "\n",
       "           row_contrib_dim3  row_contrib_dim4  row_contrib_dim5  \\\n",
       "Allemagne          8.110961          5.225067          0.009248   \n",
       "Canada             0.000197          0.083178          2.995717   \n",
       "France             0.008629          3.706053          7.709494   \n",
       "GB                 0.695668          0.495848         12.657155   \n",
       "Italie             0.017509         13.964833         23.950026   \n",
       "Japon             24.297470          7.957081          0.114450   \n",
       "Russie            20.411086          0.655407          0.117380   \n",
       "USA                0.665785          1.266654          0.000877   \n",
       "Afrique           37.434437          1.512458          0.961939   \n",
       "Amérique           4.216432          0.002475          1.565234   \n",
       "Asie               0.736303         33.766612          2.044239   \n",
       "Europe             0.077698         10.188522          4.101223   \n",
       "Océanie            3.327824         21.175813         43.773019   \n",
       "\n",
       "           row_contrib_dim6  row_cos2_dim1  row_cos2_dim2  row_cos2_dim3  \\\n",
       "Allemagne          0.141412       0.000675       0.772623       0.151226   \n",
       "Canada            11.594474       0.453026       0.044609       0.000038   \n",
       "France             3.536354       0.618357       0.278518       0.000129   \n",
       "GB                 0.977962       0.056117       0.487524       0.043802   \n",
       "Italie            21.283525       0.257549       0.013872       0.000525   \n",
       "Japon             24.119537       0.031781       0.007147       0.713150   \n",
       "Russie             0.001847       0.199759       0.540537       0.252744   \n",
       "USA                3.040235       0.940202       0.042982       0.005809   \n",
       "Afrique           20.814390       0.549971       0.001629       0.409323   \n",
       "Amérique           0.000007       0.873499       0.063439       0.053500   \n",
       "Asie               0.278968       0.575871       0.001713       0.011192   \n",
       "Europe             6.803822       0.742380       0.108413       0.001100   \n",
       "Océanie            7.407467       0.074845       0.023289       0.072536   \n",
       "\n",
       "           row_cos2_dim4  row_cos2_dim5  row_cos2_dim6  \n",
       "Allemagne       0.075147       0.000083   2.463903e-04  \n",
       "Canada          0.012462       0.279420   2.104446e-01  \n",
       "France          0.042725       0.055332   4.938996e-03  \n",
       "GB              0.024083       0.382720   5.754353e-03  \n",
       "Italie          0.323241       0.345130   5.968288e-02  \n",
       "Japon           0.180153       0.001613   6.615631e-02  \n",
       "Russie          0.006260       0.000698   2.137221e-06  \n",
       "USA             0.008525       0.000004   2.478846e-03  \n",
       "Afrique         0.012757       0.005051   2.126872e-02  \n",
       "Amérique        0.000024       0.009538   8.206865e-09  \n",
       "Asie            0.395906       0.014922   3.962540e-04  \n",
       "Europe          0.111233       0.027875   8.998924e-03  \n",
       "Océanie         0.356043       0.458199   1.508853e-02  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_pays=my_ca.row_topandas()\n",
    "info_pays"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6d891c1-247c-43b9-9ee3-927202d8bdbc",
   "metadata": {},
   "source": [
    "Coordonnées, contribution et les cosinus carré des candidats sur les axes factoriels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "819c0620-5e60-41fd-9dfa-0438ff030150",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_coord_dim1</th>\n",
       "      <th>col_coord_dim2</th>\n",
       "      <th>col_coord_dim3</th>\n",
       "      <th>col_coord_dim4</th>\n",
       "      <th>col_coord_dim5</th>\n",
       "      <th>col_coord_dim6</th>\n",
       "      <th>col_contrib_dim1</th>\n",
       "      <th>col_contrib_dim2</th>\n",
       "      <th>col_contrib_dim3</th>\n",
       "      <th>col_contrib_dim4</th>\n",
       "      <th>col_contrib_dim5</th>\n",
       "      <th>col_contrib_dim6</th>\n",
       "      <th>col_cos2_dim1</th>\n",
       "      <th>col_cos2_dim2</th>\n",
       "      <th>col_cos2_dim3</th>\n",
       "      <th>col_cos2_dim4</th>\n",
       "      <th>col_cos2_dim5</th>\n",
       "      <th>col_cos2_dim6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chimie</th>\n",
       "      <td>-0.062337</td>\n",
       "      <td>-0.151363</td>\n",
       "      <td>-0.067287</td>\n",
       "      <td>0.090850</td>\n",
       "      <td>0.040496</td>\n",
       "      <td>0.067425</td>\n",
       "      <td>0.938216</td>\n",
       "      <td>16.474083</td>\n",
       "      <td>4.275276</td>\n",
       "      <td>10.103787</td>\n",
       "      <td>3.224560</td>\n",
       "      <td>45.936459</td>\n",
       "      <td>0.084913</td>\n",
       "      <td>0.500629</td>\n",
       "      <td>0.098932</td>\n",
       "      <td>1.803541e-01</td>\n",
       "      <td>0.035834</td>\n",
       "      <td>0.099338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Economie</th>\n",
       "      <td>-0.529141</td>\n",
       "      <td>0.286614</td>\n",
       "      <td>0.155823</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>-0.132385</td>\n",
       "      <td>0.036129</td>\n",
       "      <td>31.253702</td>\n",
       "      <td>27.309217</td>\n",
       "      <td>10.600333</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>15.932451</td>\n",
       "      <td>6.098030</td>\n",
       "      <td>0.690907</td>\n",
       "      <td>0.202709</td>\n",
       "      <td>0.059916</td>\n",
       "      <td>2.145786e-08</td>\n",
       "      <td>0.043247</td>\n",
       "      <td>0.003221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Littérature</th>\n",
       "      <td>0.516502</td>\n",
       "      <td>-0.058778</td>\n",
       "      <td>0.033765</td>\n",
       "      <td>-0.151684</td>\n",
       "      <td>-0.176188</td>\n",
       "      <td>0.022564</td>\n",
       "      <td>35.513630</td>\n",
       "      <td>1.369719</td>\n",
       "      <td>0.593590</td>\n",
       "      <td>15.529598</td>\n",
       "      <td>33.654581</td>\n",
       "      <td>2.836599</td>\n",
       "      <td>0.818506</td>\n",
       "      <td>0.010600</td>\n",
       "      <td>0.003498</td>\n",
       "      <td>7.059249e-02</td>\n",
       "      <td>0.095242</td>\n",
       "      <td>0.001562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Médecine</th>\n",
       "      <td>-0.166807</td>\n",
       "      <td>-0.081065</td>\n",
       "      <td>0.041946</td>\n",
       "      <td>-0.165052</td>\n",
       "      <td>0.081818</td>\n",
       "      <td>-0.023301</td>\n",
       "      <td>7.983355</td>\n",
       "      <td>5.615373</td>\n",
       "      <td>1.974369</td>\n",
       "      <td>39.630187</td>\n",
       "      <td>15.642009</td>\n",
       "      <td>6.519350</td>\n",
       "      <td>0.393922</td>\n",
       "      <td>0.093035</td>\n",
       "      <td>0.024909</td>\n",
       "      <td>3.856762e-01</td>\n",
       "      <td>0.094771</td>\n",
       "      <td>0.007686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paix</th>\n",
       "      <td>0.340893</td>\n",
       "      <td>0.058610</td>\n",
       "      <td>0.303551</td>\n",
       "      <td>0.168632</td>\n",
       "      <td>0.094088</td>\n",
       "      <td>-0.021278</td>\n",
       "      <td>14.508999</td>\n",
       "      <td>1.277336</td>\n",
       "      <td>44.994771</td>\n",
       "      <td>18.001620</td>\n",
       "      <td>9.001403</td>\n",
       "      <td>2.365903</td>\n",
       "      <td>0.465710</td>\n",
       "      <td>0.013767</td>\n",
       "      <td>0.369270</td>\n",
       "      <td>1.139623e-01</td>\n",
       "      <td>0.035477</td>\n",
       "      <td>0.001815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Physique</th>\n",
       "      <td>-0.058320</td>\n",
       "      <td>-0.028951</td>\n",
       "      <td>-0.118792</td>\n",
       "      <td>0.102971</td>\n",
       "      <td>-0.064302</td>\n",
       "      <td>-0.054243</td>\n",
       "      <td>0.970251</td>\n",
       "      <td>0.712092</td>\n",
       "      <td>15.744042</td>\n",
       "      <td>15.335546</td>\n",
       "      <td>9.605880</td>\n",
       "      <td>35.127296</td>\n",
       "      <td>0.094398</td>\n",
       "      <td>0.023263</td>\n",
       "      <td>0.391651</td>\n",
       "      <td>2.942726e-01</td>\n",
       "      <td>0.114755</td>\n",
       "      <td>0.081660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mathématiques</th>\n",
       "      <td>0.323603</td>\n",
       "      <td>0.433685</td>\n",
       "      <td>-0.257184</td>\n",
       "      <td>-0.057203</td>\n",
       "      <td>0.137252</td>\n",
       "      <td>0.017784</td>\n",
       "      <td>8.831848</td>\n",
       "      <td>47.242179</td>\n",
       "      <td>21.817618</td>\n",
       "      <td>1.399257</td>\n",
       "      <td>12.939115</td>\n",
       "      <td>1.116362</td>\n",
       "      <td>0.274586</td>\n",
       "      <td>0.493174</td>\n",
       "      <td>0.173435</td>\n",
       "      <td>8.580157e-03</td>\n",
       "      <td>0.049396</td>\n",
       "      <td>0.000829</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               col_coord_dim1  col_coord_dim2  col_coord_dim3  col_coord_dim4  \\\n",
       "Chimie              -0.062337       -0.151363       -0.067287        0.090850   \n",
       "Economie            -0.529141        0.286614        0.155823        0.000093   \n",
       "Littérature          0.516502       -0.058778        0.033765       -0.151684   \n",
       "Médecine            -0.166807       -0.081065        0.041946       -0.165052   \n",
       "Paix                 0.340893        0.058610        0.303551        0.168632   \n",
       "Physique            -0.058320       -0.028951       -0.118792        0.102971   \n",
       "Mathématiques        0.323603        0.433685       -0.257184       -0.057203   \n",
       "\n",
       "               col_coord_dim5  col_coord_dim6  col_contrib_dim1  \\\n",
       "Chimie               0.040496        0.067425          0.938216   \n",
       "Economie            -0.132385        0.036129         31.253702   \n",
       "Littérature         -0.176188        0.022564         35.513630   \n",
       "Médecine             0.081818       -0.023301          7.983355   \n",
       "Paix                 0.094088       -0.021278         14.508999   \n",
       "Physique            -0.064302       -0.054243          0.970251   \n",
       "Mathématiques        0.137252        0.017784          8.831848   \n",
       "\n",
       "               col_contrib_dim2  col_contrib_dim3  col_contrib_dim4  \\\n",
       "Chimie                16.474083          4.275276         10.103787   \n",
       "Economie              27.309217         10.600333          0.000005   \n",
       "Littérature            1.369719          0.593590         15.529598   \n",
       "Médecine               5.615373          1.974369         39.630187   \n",
       "Paix                   1.277336         44.994771         18.001620   \n",
       "Physique               0.712092         15.744042         15.335546   \n",
       "Mathématiques         47.242179         21.817618          1.399257   \n",
       "\n",
       "               col_contrib_dim5  col_contrib_dim6  col_cos2_dim1  \\\n",
       "Chimie                 3.224560         45.936459       0.084913   \n",
       "Economie              15.932451          6.098030       0.690907   \n",
       "Littérature           33.654581          2.836599       0.818506   \n",
       "Médecine              15.642009          6.519350       0.393922   \n",
       "Paix                   9.001403          2.365903       0.465710   \n",
       "Physique               9.605880         35.127296       0.094398   \n",
       "Mathématiques         12.939115          1.116362       0.274586   \n",
       "\n",
       "               col_cos2_dim2  col_cos2_dim3  col_cos2_dim4  col_cos2_dim5  \\\n",
       "Chimie              0.500629       0.098932   1.803541e-01       0.035834   \n",
       "Economie            0.202709       0.059916   2.145786e-08       0.043247   \n",
       "Littérature         0.010600       0.003498   7.059249e-02       0.095242   \n",
       "Médecine            0.093035       0.024909   3.856762e-01       0.094771   \n",
       "Paix                0.013767       0.369270   1.139623e-01       0.035477   \n",
       "Physique            0.023263       0.391651   2.942726e-01       0.114755   \n",
       "Mathématiques       0.493174       0.173435   8.580157e-03       0.049396   \n",
       "\n",
       "               col_cos2_dim6  \n",
       "Chimie              0.099338  \n",
       "Economie            0.003221  \n",
       "Littérature         0.001562  \n",
       "Médecine            0.007686  \n",
       "Paix                0.001815  \n",
       "Physique            0.081660  \n",
       "Mathématiques       0.000829  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_TypeNobels = my_ca.col_topandas()\n",
    "info_TypeNobels "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "585c6747-3e13-417c-abab-3ecdf0fcea31",
   "metadata": {},
   "source": [
    "### 4.2.5. Analyse Graphique"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6572eeb7-690c-49b4-bb9e-5f7c3fa6a18e",
   "metadata": {},
   "source": [
    "#### 4.2.5.1. Graphique des Pays (profil ligne)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c4d91d4e-0adb-4c4a-9ad8-5eba02c79423",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graphique des pays (en ligne)\n",
    "my_ca.mapping_row(num_x_axis=1, num_y_axis=2)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "7d263730-8b78-40b5-bb49-3316c25eac08",
   "metadata": {},
   "source": [
    "Les coeffs nous montrent quer l'on peut analyser Asis,Afrique, France, Europe, Amérique, USA sur le premier axe factoriel\n",
    "cet axe montre (Dim 1) que USA, Amérique ont des Nobels de même types et sont fortement opposés l'Afrique et un peu moins avec France et Asie. \n",
    "\n",
    "Les coeffs nous montrent quer l'on peut analyser Russie Allemagne sur le second axe factoriel\n",
    "cet axe montre (Dim 2) que Allemagne et Russie sont fortement opposé."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aa1be7a-8305-453d-ace8-7ae4efe72f14",
   "metadata": {},
   "source": [
    "#### 4.2.5.2. Graphique des candidats (profil colonne)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "eff1901a-f1e2-4e58-906d-9fe6e644c155",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graphique des candidats (en colonne)\n",
    "my_ca.mapping_col(num_x_axis=1,num_y_axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8da591d3-960a-4fb7-ad26-b37867e9eae2",
   "metadata": {},
   "outputs": [],
   "source": [
    "Les coeffs nous montrent quer l'on peut analyser littérature et économie sur le premier axe factoriel\n",
    "cet axe montre (Dim 1) que littérature et économie sont fortement opposés \n",
    "\n",
    "Les coeffs nous montrent quer l'on peut analyser chimie et math sur le second axe factoriel\n",
    "cet axe montre (Dim 2) que chimie et math sont fortement opposé."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5045d71-3739-4f59-8737-8077be5469a1",
   "metadata": {},
   "source": [
    "#### 4.2.5.3. Graphique superposition des profils lignes et colonnes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f4b93807-47b6-48a4-bd76-415765b4ca23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graphique profil ligne profil colonne\n",
    "\n",
    "my_ca.mapping(1, 2, figsize=(10, 8))"
   ]
  },
  {
   "cell_type": "raw",
   "id": "b1d3955c-a199-4a56-a791-a399605c728e",
   "metadata": {},
   "source": [
    "Exemple de ce que l'on peut lire :\n",
    "Amérique, USA : Economie, chimie \n",
    "Russie, France : Mathématique\n",
    "Asie : Littérature\n",
    "Allemagne : Physique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aef7f6f7-a350-431f-ba89-01bf2322d935",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "100jML",
   "language": "python",
   "name": "100jml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}