TP01_prise_en_main.ipynb

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      "source": [
        "# Machine Learning (Lettres) TP 1 : Prise en main\n",
        "\n",
        "## Les notebooks\n",
        "\n",
        "Ce que vous avez devant les yeux est un **Jupyter Notebook**. C'est un rapport interactif, qui permet d'écrire du texte formaté (dans un langage de balises appelé **Markdown**, voir p.ex. https://www.markdowntutorial.com/) ainsi que du code **Python**, comme ci-dessous."
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        "outputId": "248ce244-8aca-4f76-86ec-d97d14c77080"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Le résultat de a + b est égal à 33\n"
          ]
        }
      ],
      "source": [
        "# Un exemple de code en Python\n",
        "a = 10\n",
        "b = 23\n",
        "print(f\"Le résultat de a + b est égal à {a + b}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vous devez normalement voir le résultat de votre sortie juste après le bloc de code. Remarquez que vous pouvez à tout moment modifier une partie du code pour changer son execution. (Faites le ! C'est en testant des choses que l'on apprend le mieux) Pour plus de détails à propros des Jupyter Notebooks https://docs.jupyter.org/en/latest/."
      ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Les notebooks peuvent s'executer en local (sur votre machine, voir https://docs.jupyter.org/en/latest/install/notebook-classic.html), mais également sur un environnement d'execution distant (un serveur). Il existe plusieurs services proposant des serveurs pour des Jupyter Notebooks, p.ex. :\n",
        "\n",
        "* Google Colab: https://colab.research.google.com/\n",
        "* Paperspace Gradient : https://www.paperspace.com/gradient\n",
        "* Kaggle : https://www.kaggle.com/\n",
        "* Amazon SageMaker : https://aws.amazon.com/fr/sagemaker/\n",
        "\n",
        "L'avantage des serveurs, c'est que l'installation (parfois fastidieuse) des différentes librairies Python est déjà faite, et qu'ils disposent de GPU (Graphical Processing Units) ou de TPU (Tensor Processing Units), utiles pour l'entrainement de nombreux modèles de Machine Learning."
      ],
      "metadata": {
        "id": "sw162GDtsP-l"
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    {
      "cell_type": "markdown",
      "source": [
        "## Chargement et manipulation des données avec `pandas`\n",
        "\n",
        "La première librairie python dont nous allons (re)voir l'utilisation est `pandas` https://pandas.pydata.org/, qui permet le chargement des fichiers de données ainsi que leur manipulation. On commence par la charger (on utilise fréquemment l'alias `pd` pour cette dernière)."
      ],
      "metadata": {
        "id": "AUfWr214x_vr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
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        "id": "2GknYnN9yqXM"
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      "execution_count": 2,
      "outputs": []
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    {
      "cell_type": "markdown",
      "source": [
        "Vérifiez que le chemin d'accès contenu dans `file_path` pointe bien sur le fichier \"iris.csv\" dans la première ligne de code de ce qui suit. Le chargement s'effectue ensuite facilement avec la fonction `pd.read_csv(file_path)` (il existe plusieurs options dans cette fonction pour, p.ex., changer le séparateur ou formater les variables)."
      ],
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        "id": "TiygbGqdzRjk"
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    {
      "cell_type": "code",
      "source": [
        "file_path = \"drive/MyDrive/Colab Notebooks/ml_data/TP1/iris.csv\"\n",
        "my_df = pd.read_csv(file_path)\n",
        "my_df"
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              "     sepal.length  sepal.width  petal.length  petal.width    variety\n",
              "0             5.1          3.5           1.4          0.2     Setosa\n",
              "1             4.9          3.0           1.4          0.2     Setosa\n",
              "2             4.7          3.2           1.3          0.2     Setosa\n",
              "3             4.6          3.1           1.5          0.2     Setosa\n",
              "4             5.0          3.6           1.4          0.2     Setosa\n",
              "..            ...          ...           ...          ...        ...\n",
              "145           6.7          3.0           5.2          2.3  Virginica\n",
              "146           6.3          2.5           5.0          1.9  Virginica\n",
              "147           6.5          3.0           5.2          2.0  Virginica\n",
              "148           6.2          3.4           5.4          2.3  Virginica\n",
              "149           5.9          3.0           5.1          1.8  Virginica\n",
              "\n",
              "[150 rows x 5 columns]"
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              "        document.querySelector('#id_d8fc5b27-fd66-49dc-a865-bb3f254c667b button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('my_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "my_df",
              "summary": "{\n  \"name\": \"my_df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal.length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.828066127977863,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal.width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4358662849366982,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal.length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7652982332594662,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal.width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7622376689603465,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"variety\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Setosa\",\n          \"Versicolor\",\n          \"Virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "L'objet `my_df` est un objet de classe **DataFrame**.\n",
        "\n"
      ],
      "metadata": {
        "id": "ftQLH3a0LwyO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "type(my_df)"
      ],
      "metadata": {
        "id": "VTKN_9iI1WCI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "46c2689b-4d23-49c3-ce24-f3ceacfd0acb"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>pandas.core.frame.DataFrame</b><br/>def __init__(data=None, index: Axes | None=None, columns: Axes | None=None, dtype: Dtype | None=None, copy: bool | None=None) -&gt; None</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py</a>Two-dimensional, size-mutable, potentially heterogeneous tabular data.\n",
              "\n",
              "Data structure also contains labeled axes (rows and columns).\n",
              "Arithmetic operations align on both row and column labels. Can be\n",
              "thought of as a dict-like container for Series objects. The primary\n",
              "pandas data structure.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame\n",
              "    Dict can contain Series, arrays, constants, dataclass or list-like objects. If\n",
              "    data is a dict, column order follows insertion-order. If a dict contains Series\n",
              "    which have an index defined, it is aligned by its index. This alignment also\n",
              "    occurs if data is a Series or a DataFrame itself. Alignment is done on\n",
              "    Series/DataFrame inputs.\n",
              "\n",
              "    If data is a list of dicts, column order follows insertion-order.\n",
              "\n",
              "index : Index or array-like\n",
              "    Index to use for resulting frame. Will default to RangeIndex if\n",
              "    no indexing information part of input data and no index provided.\n",
              "columns : Index or array-like\n",
              "    Column labels to use for resulting frame when data does not have them,\n",
              "    defaulting to RangeIndex(0, 1, 2, ..., n). If data contains column labels,\n",
              "    will perform column selection instead.\n",
              "dtype : dtype, default None\n",
              "    Data type to force. Only a single dtype is allowed. If None, infer.\n",
              "copy : bool or None, default None\n",
              "    Copy data from inputs.\n",
              "    For dict data, the default of None behaves like ``copy=True``.  For DataFrame\n",
              "    or 2d ndarray input, the default of None behaves like ``copy=False``.\n",
              "    If data is a dict containing one or more Series (possibly of different dtypes),\n",
              "    ``copy=False`` will ensure that these inputs are not copied.\n",
              "\n",
              "    .. versionchanged:: 1.3.0\n",
              "\n",
              "See Also\n",
              "--------\n",
              "DataFrame.from_records : Constructor from tuples, also record arrays.\n",
              "DataFrame.from_dict : From dicts of Series, arrays, or dicts.\n",
              "read_csv : Read a comma-separated values (csv) file into DataFrame.\n",
              "read_table : Read general delimited file into DataFrame.\n",
              "read_clipboard : Read text from clipboard into DataFrame.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "Please reference the :ref:`User Guide &lt;basics.dataframe&gt;` for more information.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "Constructing DataFrame from a dictionary.\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;col1&#x27;: [1, 2], &#x27;col2&#x27;: [3, 4]}\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=d)\n",
              "&gt;&gt;&gt; df\n",
              "   col1  col2\n",
              "0     1     3\n",
              "1     2     4\n",
              "\n",
              "Notice that the inferred dtype is int64.\n",
              "\n",
              "&gt;&gt;&gt; df.dtypes\n",
              "col1    int64\n",
              "col2    int64\n",
              "dtype: object\n",
              "\n",
              "To enforce a single dtype:\n",
              "\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=d, dtype=np.int8)\n",
              "&gt;&gt;&gt; df.dtypes\n",
              "col1    int8\n",
              "col2    int8\n",
              "dtype: object\n",
              "\n",
              "Constructing DataFrame from a dictionary including Series:\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;col1&#x27;: [0, 1, 2, 3], &#x27;col2&#x27;: pd.Series([2, 3], index=[2, 3])}\n",
              "&gt;&gt;&gt; pd.DataFrame(data=d, index=[0, 1, 2, 3])\n",
              "   col1  col2\n",
              "0     0   NaN\n",
              "1     1   NaN\n",
              "2     2   2.0\n",
              "3     3   3.0\n",
              "\n",
              "Constructing DataFrame from numpy ndarray:\n",
              "\n",
              "&gt;&gt;&gt; df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),\n",
              "...                    columns=[&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;])\n",
              "&gt;&gt;&gt; df2\n",
              "   a  b  c\n",
              "0  1  2  3\n",
              "1  4  5  6\n",
              "2  7  8  9\n",
              "\n",
              "Constructing DataFrame from a numpy ndarray that has labeled columns:\n",
              "\n",
              "&gt;&gt;&gt; data = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],\n",
              "...                 dtype=[(&quot;a&quot;, &quot;i4&quot;), (&quot;b&quot;, &quot;i4&quot;), (&quot;c&quot;, &quot;i4&quot;)])\n",
              "&gt;&gt;&gt; df3 = pd.DataFrame(data, columns=[&#x27;c&#x27;, &#x27;a&#x27;])\n",
              "...\n",
              "&gt;&gt;&gt; df3\n",
              "   c  a\n",
              "0  3  1\n",
              "1  6  4\n",
              "2  9  7\n",
              "\n",
              "Constructing DataFrame from dataclass:\n",
              "\n",
              "&gt;&gt;&gt; from dataclasses import make_dataclass\n",
              "&gt;&gt;&gt; Point = make_dataclass(&quot;Point&quot;, [(&quot;x&quot;, int), (&quot;y&quot;, int)])\n",
              "&gt;&gt;&gt; pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])\n",
              "   x  y\n",
              "0  0  0\n",
              "1  0  3\n",
              "2  2  3\n",
              "\n",
              "Constructing DataFrame from Series/DataFrame:\n",
              "\n",
              "&gt;&gt;&gt; ser = pd.Series([1, 2, 3], index=[&quot;a&quot;, &quot;b&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=ser, index=[&quot;a&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df\n",
              "   0\n",
              "a  1\n",
              "c  3\n",
              "\n",
              "&gt;&gt;&gt; df1 = pd.DataFrame([1, 2, 3], index=[&quot;a&quot;, &quot;b&quot;, &quot;c&quot;], columns=[&quot;x&quot;])\n",
              "&gt;&gt;&gt; df2 = pd.DataFrame(data=df1, index=[&quot;a&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df2\n",
              "   x\n",
              "a  1\n",
              "c  3</pre>\n",
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              "      </div>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cet objet possède un index (pour lister les lignes), des noms de columns, et les types de ses variables."
      ],
      "metadata": {
        "id": "r9kMDA_rORid"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(my_df.index)\n",
        "print(my_df.columns)\n",
        "print(my_df.dtypes)"
      ],
      "metadata": {
        "id": "B672xn7fL2vD",
        "outputId": "46d00400-9488-43fd-aa47-a68fa70b2106",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RangeIndex(start=0, stop=150, step=1)\n",
            "Index(['sepal.length', 'sepal.width', 'petal.length', 'petal.width',\n",
            "       'variety'],\n",
            "      dtype='object')\n",
            "sepal.length    float64\n",
            "sepal.width     float64\n",
            "petal.length    float64\n",
            "petal.width     float64\n",
            "variety          object\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Il est possible d'accéder aux lignes et colonnes (variables) grâce à leur nom (Notez qu'ici, les lignes n'ont pas de nom, on utilise alors le \"slicing\")."
      ],
      "metadata": {
        "id": "E5roAmsFNR9H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "my_df[\"sepal.length\"]"
      ],
      "metadata": {
        "id": "x106vEE5NOPz",
        "outputId": "d8516cbd-1339-437a-de60-ac7afe6ef244",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      5.1\n",
              "1      4.9\n",
              "2      4.7\n",
              "3      4.6\n",
              "4      5.0\n",
              "      ... \n",
              "145    6.7\n",
              "146    6.3\n",
              "147    6.5\n",
              "148    6.2\n",
              "149    5.9\n",
              "Name: sepal.length, Length: 150, dtype: float64"
            ],
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              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
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              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
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              "      <td>6.3</td>\n",
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              "      <td>6.5</td>\n",
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              "      <th>148</th>\n",
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              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_df[1:3]"
      ],
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          "height": 112
        },
        "id": "7LVBKH19RGqc",
        "outputId": "1930de30-92bf-4dbd-fe95-08fe7c0262a6"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sepal.length  sepal.width  petal.length  petal.width variety\n",
              "1           4.9          3.0           1.4          0.2  Setosa\n",
              "2           4.7          3.2           1.3          0.2  Setosa"
            ],
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              "\n",
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              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"my_df[1:3]\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"sepal.length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14142135623730964,\n        \"min\": 4.7,\n        \"max\": 4.9,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4.7,\n          4.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal.width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14142135623730964,\n        \"min\": 3.0,\n        \"max\": 3.2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3.2,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal.length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07071067811865465,\n        \"min\": 1.3,\n        \"max\": 1.4,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.3,\n          1.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal.width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.2,\n        \"max\": 0.2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"variety\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Setosa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Notez qu'une colonne est un objet de la classe **Series**."
      ],
      "metadata": {
        "id": "8te0ZfvON2FE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "type(my_df[\"sepal.length\"])"
      ],
      "metadata": {
        "id": "O3uvT6n2NhYC",
        "outputId": "a6f22832-d932-4585-bbaa-fc83d5bf9466",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
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              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>pandas.core.series.Series</b><br/>def __init__(data=None, index=None, dtype: Dtype | None=None, name=None, copy: bool | None=None, fastpath: bool=False) -&gt; None</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/pandas/core/series.py</a>One-dimensional ndarray with axis labels (including time series).\n",
              "\n",
              "Labels need not be unique but must be a hashable type. The object\n",
              "supports both integer- and label-based indexing and provides a host of\n",
              "methods for performing operations involving the index. Statistical\n",
              "methods from ndarray have been overridden to automatically exclude\n",
              "missing data (currently represented as NaN).\n",
              "\n",
              "Operations between Series (+, -, /, \\*, \\*\\*) align values based on their\n",
              "associated index values-- they need not be the same length. The result\n",
              "index will be the sorted union of the two indexes.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "data : array-like, Iterable, dict, or scalar value\n",
              "    Contains data stored in Series. If data is a dict, argument order is\n",
              "    maintained.\n",
              "index : array-like or Index (1d)\n",
              "    Values must be hashable and have the same length as `data`.\n",
              "    Non-unique index values are allowed. Will default to\n",
              "    RangeIndex (0, 1, 2, ..., n) if not provided. If data is dict-like\n",
              "    and index is None, then the keys in the data are used as the index. If the\n",
              "    index is not None, the resulting Series is reindexed with the index values.\n",
              "dtype : str, numpy.dtype, or ExtensionDtype, optional\n",
              "    Data type for the output Series. If not specified, this will be\n",
              "    inferred from `data`.\n",
              "    See the :ref:`user guide &lt;basics.dtypes&gt;` for more usages.\n",
              "name : Hashable, default None\n",
              "    The name to give to the Series.\n",
              "copy : bool, default False\n",
              "    Copy input data. Only affects Series or 1d ndarray input. See examples.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "Please reference the :ref:`User Guide &lt;basics.series&gt;` for more information.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "Constructing Series from a dictionary with an Index specified\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;a&#x27;: 1, &#x27;b&#x27;: 2, &#x27;c&#x27;: 3}\n",
              "&gt;&gt;&gt; ser = pd.Series(data=d, index=[&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;])\n",
              "&gt;&gt;&gt; ser\n",
              "a   1\n",
              "b   2\n",
              "c   3\n",
              "dtype: int64\n",
              "\n",
              "The keys of the dictionary match with the Index values, hence the Index\n",
              "values have no effect.\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;a&#x27;: 1, &#x27;b&#x27;: 2, &#x27;c&#x27;: 3}\n",
              "&gt;&gt;&gt; ser = pd.Series(data=d, index=[&#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;])\n",
              "&gt;&gt;&gt; ser\n",
              "x   NaN\n",
              "y   NaN\n",
              "z   NaN\n",
              "dtype: float64\n",
              "\n",
              "Note that the Index is first build with the keys from the dictionary.\n",
              "After this the Series is reindexed with the given Index values, hence we\n",
              "get all NaN as a result.\n",
              "\n",
              "Constructing Series from a list with `copy=False`.\n",
              "\n",
              "&gt;&gt;&gt; r = [1, 2]\n",
              "&gt;&gt;&gt; ser = pd.Series(r, copy=False)\n",
              "&gt;&gt;&gt; ser.iloc[0] = 999\n",
              "&gt;&gt;&gt; r\n",
              "[1, 2]\n",
              "&gt;&gt;&gt; ser\n",
              "0    999\n",
              "1      2\n",
              "dtype: int64\n",
              "\n",
              "Due to input data type the Series has a `copy` of\n",
              "the original data even though `copy=False`, so\n",
              "the data is unchanged.\n",
              "\n",
              "Constructing Series from a 1d ndarray with `copy=False`.\n",
              "\n",
              "&gt;&gt;&gt; r = np.array([1, 2])\n",
              "&gt;&gt;&gt; ser = pd.Series(r, copy=False)\n",
              "&gt;&gt;&gt; ser.iloc[0] = 999\n",
              "&gt;&gt;&gt; r\n",
              "array([999,   2])\n",
              "&gt;&gt;&gt; ser\n",
              "0    999\n",
              "1      2\n",
              "dtype: int64\n",
              "\n",
              "Due to input data type the Series has a `view` on\n",
              "the original data, so\n",
              "the data is changed as well.</pre>\n",
              "      <script>\n",
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              "      </script>\n",
              "      </div>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plusieurs opérations sont possibles sur ce type d'objet."
      ],
      "metadata": {
        "id": "ARwXcZI8Pmn8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "my_df[\"sepal.length\"] + my_df[\"sepal.width\"]"
      ],
      "metadata": {
        "id": "rFs2SejaN0nl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "34f2a86c-e346-436b-eb54-f4c309b32e65"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      8.6\n",
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              "      ... \n",
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              "      <th>4</th>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>145</th>\n",
              "      <td>9.7</td>\n",
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              "<p>150 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_df[\"sepal.length\"] < 5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "wM3b5EiuPk31",
        "outputId": "ed143764-928a-453c-91e3-db30cd42d682"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      False\n",
              "1       True\n",
              "2       True\n",
              "3       True\n",
              "4      False\n",
              "       ...  \n",
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              "148    False\n",
              "149    False\n",
              "Name: sepal.length, Length: 150, dtype: bool"
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              "<div>\n",
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              "      <th>2</th>\n",
              "      <td>True</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>True</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
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              "      <td>...</td>\n",
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              "      <th>145</th>\n",
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              "      <th>146</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> bool</label>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ajouter une colonne à notre DataFrame est également très aisé."
      ],
      "metadata": {
        "id": "Lw9ibZkvQImc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "my_df[\"sepal.length.cat\"] = my_df[\"sepal.length\"] < 5\n",
        "my_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "KNn5lA8iP430",
        "outputId": "b4a69104-bf75-469e-9ba9-44275e059aa1"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     sepal.length  sepal.width  petal.length  petal.width    variety  \\\n",
              "0             5.1          3.5           1.4          0.2     Setosa   \n",
              "1             4.9          3.0           1.4          0.2     Setosa   \n",
              "2             4.7          3.2           1.3          0.2     Setosa   \n",
              "3             4.6          3.1           1.5          0.2     Setosa   \n",
              "4             5.0          3.6           1.4          0.2     Setosa   \n",
              "..            ...          ...           ...          ...        ...   \n",
              "145           6.7          3.0           5.2          2.3  Virginica   \n",
              "146           6.3          2.5           5.0          1.9  Virginica   \n",
              "147           6.5          3.0           5.2          2.0  Virginica   \n",
              "148           6.2          3.4           5.4          2.3  Virginica   \n",
              "149           5.9          3.0           5.1          1.8  Virginica   \n",
              "\n",
              "     sepal.length.cat  \n",
              "0               False  \n",
              "1                True  \n",
              "2                True  \n",
              "3                True  \n",
              "4               False  \n",
              "..                ...  \n",
              "145             False  \n",
              "146             False  \n",
              "147             False  \n",
              "148             False  \n",
              "149             False  \n",
              "\n",
              "[150 rows x 6 columns]"
            ],
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              "      <th>3</th>\n",
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              "      <td>0.2</td>\n",
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              "      <td>True</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>145</th>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
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              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Virginica</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Virginica</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 6 columns</p>\n",
              "</div>\n",
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              "\n",
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              "type": "dataframe",
              "variable_name": "my_df",
              "summary": "{\n  \"name\": \"my_df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal.length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.828066127977863,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal.width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4358662849366982,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal.length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7652982332594662,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal.width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7622376689603465,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"variety\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Setosa\",\n          \"Versicolor\",\n          \"Virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal.length.cat\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pour sélectionner plusieurs lignes et colonnes, on peut utiliser `my_df.loc`, qui va sélectionner en utilisant les labels."
      ],
      "metadata": {
        "id": "Ii6k2USMRiXp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "my_df.loc[1:5, [\"sepal.length\", \"sepal.length.cat\"]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "l-XY6GLuQWFp",
        "outputId": "11d772eb-78bc-41c7-ee6f-0f49b13b9526"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sepal.length  sepal.length.cat\n",
              "1           4.9              True\n",
              "2           4.7              True\n",
              "3           4.6              True\n",
              "4           5.0             False\n",
              "5           5.4             False"
            ],
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              "      <td>4.6</td>\n",
              "      <td>True</td>\n",
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              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5.4</td>\n",
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              "        document.querySelector('#df-0b660cba-16c9-41da-abfa-2faf020ed6da button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0b660cba-16c9-41da-abfa-2faf020ed6da');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"my_df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"sepal.length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3114482300479489,\n        \"min\": 4.6,\n        \"max\": 5.4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4.7,\n          5.4,\n          4.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal.length.cat\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ou avec `my_df.iloc` pour faire des accessions avec des indices."
      ],
      "metadata": {
        "id": "rZd4RDftSv2W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "my_df.iloc[:, 3:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "yQ8bXPf3Qvz4",
        "outputId": "a865a290-6061-4545-918a-8c8f21c6b2d1"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     petal.width    variety\n",
              "0            0.2     Setosa\n",
              "1            0.2     Setosa\n",
              "2            0.2     Setosa\n",
              "3            0.2     Setosa\n",
              "4            0.2     Setosa\n",
              "..           ...        ...\n",
              "145          2.3  Virginica\n",
              "146          1.9  Virginica\n",
              "147          2.0  Virginica\n",
              "148          2.3  Virginica\n",
              "149          1.8  Virginica\n",
              "\n",
              "[150 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
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              "      <th>petal.width</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.2</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.2</td>\n",
              "      <td>Setosa</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>Setosa</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.2</td>\n",
              "      <td>Setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.2</td>\n",
              "      <td>Setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>2.3</td>\n",
              "      <td>Virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>1.9</td>\n",
              "      <td>Virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>2.0</td>\n",
              "      <td>Virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>2.3</td>\n",
              "      <td>Virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>1.8</td>\n",
              "      <td>Virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 2 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "\n",
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              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-961903fe-e34c-425b-97a3-4eacd3e5d65c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d413a062-99c4-4b64-a3d2-4d740e153c0c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d413a062-99c4-4b64-a3d2-4d740e153c0c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d413a062-99c4-4b64-a3d2-4d740e153c0c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"my_df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"petal.width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7622376689603465,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"variety\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Setosa\",\n          \"Versicolor\",\n          \"Virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "On peut égalament transformer un Dataframe en un objet `numpy.ndarray`, propice aux opérations mathématiques."
      ],
      "metadata": {
        "id": "dh12qcY3TDXA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "my_df.iloc[:, :4].to_numpy()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WruV_lXQS-3-",
        "outputId": "7656ddf3-4660-4ecd-d0ae-ca8ce78d5723"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.1, 3.5, 1.4, 0.2],\n",
              "       [4.9, 3. , 1.4, 0.2],\n",
              "       [4.7, 3.2, 1.3, 0.2],\n",
              "       [4.6, 3.1, 1.5, 0.2],\n",
              "       [5. , 3.6, 1.4, 0.2],\n",
              "       [5.4, 3.9, 1.7, 0.4],\n",
              "       [4.6, 3.4, 1.4, 0.3],\n",
              "       [5. , 3.4, 1.5, 0.2],\n",
              "       [4.4, 2.9, 1.4, 0.2],\n",
              "       [4.9, 3.1, 1.5, 0.1],\n",
              "       [5.4, 3.7, 1.5, 0.2],\n",
              "       [4.8, 3.4, 1.6, 0.2],\n",
              "       [4.8, 3. , 1.4, 0.1],\n",
              "       [4.3, 3. , 1.1, 0.1],\n",
              "       [5.8, 4. , 1.2, 0.2],\n",
              "       [5.7, 4.4, 1.5, 0.4],\n",
              "       [5.4, 3.9, 1.3, 0.4],\n",
              "       [5.1, 3.5, 1.4, 0.3],\n",
              "       [5.7, 3.8, 1.7, 0.3],\n",
              "       [5.1, 3.8, 1.5, 0.3],\n",
              "       [5.4, 3.4, 1.7, 0.2],\n",
              "       [5.1, 3.7, 1.5, 0.4],\n",
              "       [4.6, 3.6, 1. , 0.2],\n",
              "       [5.1, 3.3, 1.7, 0.5],\n",
              "       [4.8, 3.4, 1.9, 0.2],\n",
              "       [5. , 3. , 1.6, 0.2],\n",
              "       [5. , 3.4, 1.6, 0.4],\n",
              "       [5.2, 3.5, 1.5, 0.2],\n",
              "       [5.2, 3.4, 1.4, 0.2],\n",
              "       [4.7, 3.2, 1.6, 0.2],\n",
              "       [4.8, 3.1, 1.6, 0.2],\n",
              "       [5.4, 3.4, 1.5, 0.4],\n",
              "       [5.2, 4.1, 1.5, 0.1],\n",
              "       [5.5, 4.2, 1.4, 0.2],\n",
              "       [4.9, 3.1, 1.5, 0.2],\n",
              "       [5. , 3.2, 1.2, 0.2],\n",
              "       [5.5, 3.5, 1.3, 0.2],\n",
              "       [4.9, 3.6, 1.4, 0.1],\n",
              "       [4.4, 3. , 1.3, 0.2],\n",
              "       [5.1, 3.4, 1.5, 0.2],\n",
              "       [5. , 3.5, 1.3, 0.3],\n",
              "       [4.5, 2.3, 1.3, 0.3],\n",
              "       [4.4, 3.2, 1.3, 0.2],\n",
              "       [5. , 3.5, 1.6, 0.6],\n",
              "       [5.1, 3.8, 1.9, 0.4],\n",
              "       [4.8, 3. , 1.4, 0.3],\n",
              "       [5.1, 3.8, 1.6, 0.2],\n",
              "       [4.6, 3.2, 1.4, 0.2],\n",
              "       [5.3, 3.7, 1.5, 0.2],\n",
              "       [5. , 3.3, 1.4, 0.2],\n",
              "       [7. , 3.2, 4.7, 1.4],\n",
              "       [6.4, 3.2, 4.5, 1.5],\n",
              "       [6.9, 3.1, 4.9, 1.5],\n",
              "       [5.5, 2.3, 4. , 1.3],\n",
              "       [6.5, 2.8, 4.6, 1.5],\n",
              "       [5.7, 2.8, 4.5, 1.3],\n",
              "       [6.3, 3.3, 4.7, 1.6],\n",
              "       [4.9, 2.4, 3.3, 1. ],\n",
              "       [6.6, 2.9, 4.6, 1.3],\n",
              "       [5.2, 2.7, 3.9, 1.4],\n",
              "       [5. , 2. , 3.5, 1. ],\n",
              "       [5.9, 3. , 4.2, 1.5],\n",
              "       [6. , 2.2, 4. , 1. ],\n",
              "       [6.1, 2.9, 4.7, 1.4],\n",
              "       [5.6, 2.9, 3.6, 1.3],\n",
              "       [6.7, 3.1, 4.4, 1.4],\n",
              "       [5.6, 3. , 4.5, 1.5],\n",
              "       [5.8, 2.7, 4.1, 1. ],\n",
              "       [6.2, 2.2, 4.5, 1.5],\n",
              "       [5.6, 2.5, 3.9, 1.1],\n",
              "       [5.9, 3.2, 4.8, 1.8],\n",
              "       [6.1, 2.8, 4. , 1.3],\n",
              "       [6.3, 2.5, 4.9, 1.5],\n",
              "       [6.1, 2.8, 4.7, 1.2],\n",
              "       [6.4, 2.9, 4.3, 1.3],\n",
              "       [6.6, 3. , 4.4, 1.4],\n",
              "       [6.8, 2.8, 4.8, 1.4],\n",
              "       [6.7, 3. , 5. , 1.7],\n",
              "       [6. , 2.9, 4.5, 1.5],\n",
              "       [5.7, 2.6, 3.5, 1. ],\n",
              "       [5.5, 2.4, 3.8, 1.1],\n",
              "       [5.5, 2.4, 3.7, 1. ],\n",
              "       [5.8, 2.7, 3.9, 1.2],\n",
              "       [6. , 2.7, 5.1, 1.6],\n",
              "       [5.4, 3. , 4.5, 1.5],\n",
              "       [6. , 3.4, 4.5, 1.6],\n",
              "       [6.7, 3.1, 4.7, 1.5],\n",
              "       [6.3, 2.3, 4.4, 1.3],\n",
              "       [5.6, 3. , 4.1, 1.3],\n",
              "       [5.5, 2.5, 4. , 1.3],\n",
              "       [5.5, 2.6, 4.4, 1.2],\n",
              "       [6.1, 3. , 4.6, 1.4],\n",
              "       [5.8, 2.6, 4. , 1.2],\n",
              "       [5. , 2.3, 3.3, 1. ],\n",
              "       [5.6, 2.7, 4.2, 1.3],\n",
              "       [5.7, 3. , 4.2, 1.2],\n",
              "       [5.7, 2.9, 4.2, 1.3],\n",
              "       [6.2, 2.9, 4.3, 1.3],\n",
              "       [5.1, 2.5, 3. , 1.1],\n",
              "       [5.7, 2.8, 4.1, 1.3],\n",
              "       [6.3, 3.3, 6. , 2.5],\n",
              "       [5.8, 2.7, 5.1, 1.9],\n",
              "       [7.1, 3. , 5.9, 2.1],\n",
              "       [6.3, 2.9, 5.6, 1.8],\n",
              "       [6.5, 3. , 5.8, 2.2],\n",
              "       [7.6, 3. , 6.6, 2.1],\n",
              "       [4.9, 2.5, 4.5, 1.7],\n",
              "       [7.3, 2.9, 6.3, 1.8],\n",
              "       [6.7, 2.5, 5.8, 1.8],\n",
              "       [7.2, 3.6, 6.1, 2.5],\n",
              "       [6.5, 3.2, 5.1, 2. ],\n",
              "       [6.4, 2.7, 5.3, 1.9],\n",
              "       [6.8, 3. , 5.5, 2.1],\n",
              "       [5.7, 2.5, 5. , 2. ],\n",
              "       [5.8, 2.8, 5.1, 2.4],\n",
              "       [6.4, 3.2, 5.3, 2.3],\n",
              "       [6.5, 3. , 5.5, 1.8],\n",
              "       [7.7, 3.8, 6.7, 2.2],\n",
              "       [7.7, 2.6, 6.9, 2.3],\n",
              "       [6. , 2.2, 5. , 1.5],\n",
              "       [6.9, 3.2, 5.7, 2.3],\n",
              "       [5.6, 2.8, 4.9, 2. ],\n",
              "       [7.7, 2.8, 6.7, 2. ],\n",
              "       [6.3, 2.7, 4.9, 1.8],\n",
              "       [6.7, 3.3, 5.7, 2.1],\n",
              "       [7.2, 3.2, 6. , 1.8],\n",
              "       [6.2, 2.8, 4.8, 1.8],\n",
              "       [6.1, 3. , 4.9, 1.8],\n",
              "       [6.4, 2.8, 5.6, 2.1],\n",
              "       [7.2, 3. , 5.8, 1.6],\n",
              "       [7.4, 2.8, 6.1, 1.9],\n",
              "       [7.9, 3.8, 6.4, 2. ],\n",
              "       [6.4, 2.8, 5.6, 2.2],\n",
              "       [6.3, 2.8, 5.1, 1.5],\n",
              "       [6.1, 2.6, 5.6, 1.4],\n",
              "       [7.7, 3. , 6.1, 2.3],\n",
              "       [6.3, 3.4, 5.6, 2.4],\n",
              "       [6.4, 3.1, 5.5, 1.8],\n",
              "       [6. , 3. , 4.8, 1.8],\n",
              "       [6.9, 3.1, 5.4, 2.1],\n",
              "       [6.7, 3.1, 5.6, 2.4],\n",
              "       [6.9, 3.1, 5.1, 2.3],\n",
              "       [5.8, 2.7, 5.1, 1.9],\n",
              "       [6.8, 3.2, 5.9, 2.3],\n",
              "       [6.7, 3.3, 5.7, 2.5],\n",
              "       [6.7, 3. , 5.2, 2.3],\n",
              "       [6.3, 2.5, 5. , 1.9],\n",
              "       [6.5, 3. , 5.2, 2. ],\n",
              "       [6.2, 3.4, 5.4, 2.3],\n",
              "       [5.9, 3. , 5.1, 1.8]])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bien entendu, il existe encore de nombreuses fonctionnalités dans `pandas`. Pour en savoir plus, la communauté propose de nombreux tutoriels https://pandas.pydata.org/docs/getting_started/tutorials.html."
      ],
      "metadata": {
        "id": "J6VnmjSZT8fO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Opérations mathématiques avec `numpy`\n",
        "\n",
        "La librairie `numpy` https://numpy.org/ est la référence pour tout ce qui est opérations mathématiques et est utilisé de manière quasiment systématique dans tout projet de Machine Learning. On l'importe fréquemment sous l'alias `np`.\n",
        "\n"
      ],
      "metadata": {
        "id": "aRza-ceaTuuK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "K_S7ZjOxVING"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Les objets essentiels dans numpy, permettant de représenter les vecteurs ou les matrices, sont les **Arrays** (`numpy.array`). Elles peuvent être créées à partir de listes (ou listes de listes pour un tableau)."
      ],
      "metadata": {
        "id": "IvP822PJWUwG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.array([1, 2, 3, 4, 5, 6])\n",
        "b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])\n",
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JyXuY_PqTLIV",
        "outputId": "e064e2d6-fbc6-4723-98e9-a4253f9dfd0f"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 4, 5, 6])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-qrPefUhWqLc",
        "outputId": "08b35959-b6ea-4d0c-cd63-37209103d0cc"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1,  2,  3,  4],\n",
              "       [ 5,  6,  7,  8],\n",
              "       [ 9, 10, 11, 12]])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "On peut obtenir plusieurs information sur une Array."
      ],
      "metadata": {
        "id": "q5Uus0auW47G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "b.ndim # Combien d'axes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GMKBdvk9WyGX",
        "outputId": "92814774-d534-439d-b15e-03da246500f0"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b.size # Combien d'éléments"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d3UIbF3gX0fN",
        "outputId": "70d28de8-afc2-4fa1-96b6-9aa514c88bd4"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b.shape # Sa forme"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GmFPacXCXlaT",
        "outputId": "0b794739-317a-483a-baaa-68a988fec482"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b.dtype # Le type de ses éléments"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W5v8_sexXt45",
        "outputId": "1723d831-fb50-4944-f428-2e2e864facdb"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('int64')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Les crochets permettent l'accession et la modification."
      ],
      "metadata": {
        "id": "S38rrOdFZTcJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "b[:3, :3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ltM_VuHaZW3j",
        "outputId": "de5bb054-e89f-4040-ed7b-5255c295968b"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1,  2,  3],\n",
              "       [ 5,  6,  7],\n",
              "       [ 9, 10, 11]])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b[0, 0] = 100\n",
        "b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "78UQFHrYZfXZ",
        "outputId": "325738b8-48a5-44f9-8565-fd76ad59ef21"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[100,   2,   3,   4],\n",
              "       [  5,   6,   7,   8],\n",
              "       [  9,  10,  11,  12]])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Il est aussi possible de les redimmensionner les Arrays,"
      ],
      "metadata": {
        "id": "CF8mtpaYYBJ3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "c = b.reshape([2, 6])\n",
        "c"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qzrAjRP2X4sQ",
        "outputId": "3d4ab4b0-3fe7-418e-b3f4-09caef3dd5d4"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[100,   2,   3,   4,   5,   6],\n",
              "       [  7,   8,   9,  10,  11,  12]])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "de les transposer,"
      ],
      "metadata": {
        "id": "ql9Q9yBMZq5V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "c.T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hn9-EQ8rZtUp",
        "outputId": "9be4cfe0-e9cf-4187-c763-823bad210bbc"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[100,   7],\n",
              "       [  2,   8],\n",
              "       [  3,   9],\n",
              "       [  4,  10],\n",
              "       [  5,  11],\n",
              "       [  6,  12]])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "et de faire de nombreuses opérations mathématiques sur ces dernières. Les opérateurs classiques agissent sur chaque élément."
      ],
      "metadata": {
        "id": "NQpnD_ApYQ-F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "b / 10"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mdC-vBFHYLJe",
        "outputId": "1e1a12b3-65d8-4dd1-d6b6-96782c7a302b"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10. ,  0.2,  0.3,  0.4],\n",
              "       [ 0.5,  0.6,  0.7,  0.8],\n",
              "       [ 0.9,  1. ,  1.1,  1.2]])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(c.shape)\n",
        "print(a.shape)\n",
        "c + a # L'objet le plus petit (a) est dupliqué pour avoir une taille suffisante"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FLgGRNnnYl9K",
        "outputId": "1f4dcaee-6a5f-41e1-ac5e-2f40971a3888"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2, 6)\n",
            "(6,)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[101,   4,   6,   8,  10,  12],\n",
              "       [  8,  10,  12,  14,  16,  18]])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.cos(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3KPyJyjLYrM_",
        "outputId": "d1990800-697d-48c1-9c05-f540451f6897"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.54030231, -0.41614684, -0.9899925 , -0.65364362,  0.28366219,\n",
              "        0.96017029])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "L'opérateur `@` effectue la multiplication matricielle."
      ],
      "metadata": {
        "id": "6oU5-Jj6ZCsg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "e = np.array([1, 10, 100, 1000])\n",
        "b @ e"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hIsL7FKNY8q_",
        "outputId": "8ae72ba9-8f35-4f5b-bc74-bee7cd91c1de"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 4420,  8765, 13209])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`b` est (3 x 4), `e` est (4 x 1), l'opération est donc valide et le résultat est bien de dimension (3 x 1)."
      ],
      "metadata": {
        "id": "7rnObh_wafhg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Il existe encore de nombreuses fonctionnalités dans `numpy`, pour voir plus loin https://numpy.org/doc/stable/user/whatisnumpy.html."
      ],
      "metadata": {
        "id": "hV9K1OxGaqFb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Graphiques avec `matplotlib`"
      ],
      "metadata": {
        "id": "BN9sA43Fa7dJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "La dernière librairie que nous allons voir aujourd'hui est `matplotlib` https://matplotlib.org/, qui permet l'affichage de nombreux types de graphiques. On importe généralement uniquement son module `pyplot` avec l'alias `plt`."
      ],
      "metadata": {
        "id": "yUE4FHwjbTBO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "AQ062Mb-a5eM"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cette librairie est longue à prendre en main et nous n'avons malheureusement pas le temps de la voir en détail. Nous verrons de nombreux exemples d'utilisation durant ce cours ce qui permettra de voir son fonctionnement. Par exemple, pour faire un nuage de points (scatterplot) de nos Iris, on peut faire :"
      ],
      "metadata": {
        "id": "DZVA9mghcz-m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Création d'un dictionnaire pour les couleurs\n",
        "colors = {\"Setosa\": \"blue\", \"Versicolor\": \"green\", \"Virginica\": \"red\"}\n",
        "\n",
        "# Création des objets servant à l'affichage graphique\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "# On fait une boucle sur les espèces\n",
        "for species in my_df[\"variety\"].unique():\n",
        "\n",
        "  # Selection de l'espèce\n",
        "  species_df = my_df[my_df[\"variety\"] == species]\n",
        "\n",
        "  # Scatter plot de l'espèce\n",
        "  ax.scatter(x=species_df[\"sepal.length\"],\n",
        "            y=species_df[\"sepal.width\"],\n",
        "            c=colors[species],\n",
        "            label=species)\n",
        "\n",
        "# Ajout du nom des axes, d'un titre, d'une légende, et d'une grille\n",
        "ax.set_xlabel(\"Sepal Length\")\n",
        "ax.set_ylabel(\"Sepal Width\")\n",
        "ax.set_title(\"Iris\")\n",
        "ax.legend()\n",
        "ax.grid(True)\n",
        "\n",
        "plt.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "aBoK8OuMbt_Y",
        "outputId": "08f7fced-643e-4f40-e782-aed81123757e"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Si vous voulez une réelle prise en main, vous pouvez suivre les tutoriels à https://matplotlib.org/stable/tutorials/index.html."
      ],
      "metadata": {
        "id": "8skzNa17jVX3"
      }
    }
  ]
}