Business-analysis.ipynb

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        "<br>\n",
        "<body>\n",
        "    <p style=\"font-size:50px;text-align:center; color:#0070AD\"><b>Data Challenge 2023</b></p>\n",
        "    <p style=\"font-size:35px;text-align:center; color:#12ABDB\">Welcoming Notebook</p>\n",
        "    <p><center><img src=\"./src/figures/mines_invent_logo.png\" style=\"width:80%;height:auto\"></center></p>\n",
        "</body>\n",
        "<p style=\"font-size:30px;color:#0070AD\">I. Challenge Background </p>\n",
        "<br>\n",
        "\n",
        "<p style=\"text-align: justify\">Since the signing of the Kyoto Protocol in 1997, the reduction of greenhouse gases and in particular CO$_2$ emissions has become a major issue for all private and public actors. This is a key point if we hope to meet the objectives of the 2015 Paris Conference on Global Warming (COP21) to limit global warming between 1.5°C and 2°C.</p>\n",
        "\n",
        "<p style=\"text-align: justify\">In this context, many companies take into account ESG (Environmental Social Governance) criteria in their long-term investment strategy to quantify the impact of the company's actions on the environment and society.</p>\n",
        "\n",
        "<br>\n",
        "<p style=\"font-size:30px;color:#0070AD\">II. Challenge goals </p>\n",
        "<br>\n",
        "\n",
        "<p style=\"text-align: justify\">A client wishing to improve the consideration of ESG criteria for his company calls on you to estimate the carbon emissions of the vehicles used by his employees to come to their workplace. Aware that it is necessary to take into account the carbon impact of its employees, the client would like you to provide a model capable of quickly estimating the carbon emission of a vehicle based on images from a video surveillance of the company's parking lot.</p>\n",
        "\n",
        "<p style=\"text-align: justify\">The images available contain only one vehicle and for each one the coordinates of a box locating the vehicle in the image are given.</p>\n",
        "\n",
        "<p style=\"text-align: justify\">Your model has to return as outputs the four coordinates of the box locating the vehicle and the vehicle's CO$_2$ carbon emission. Indeed given an image i containing a car, the output of the global model is as follows :</p>\n",
        "    \n",
        "<br>\n",
        "<center>\n",
        "    $(\\hat{x}^{min}_{i},\\hat{x}^{max}_{i},\\hat{y}^{min}_{i},\\hat{y}^{max}_{i},\\hat{e}^{carbone}_{i})$\n",
        "</center>\n",
        "<br>  \n",
        "\n",
        "Where :\n",
        "- $\\hat{x}^{min}_{i}, \\hat{y}^{min}_{i}$ predicted coordinates of top left corner of the box locating the vehicle.\n",
        "- $\\hat{x}^{max}_{i}, \\hat{y}^{max}_{i}$ predicted coordinates of the bottom right corner of the box locating the vehicle.\n",
        "    \n",
        "To assess the results, we compute the following metric :</p>\n",
        "\n",
        "<br>\n",
        "<center>\n",
        "    $\\frac{1}{n}\\sum_{i=1}^{n}{0.7\\cdot{(1-IoU_i)} + 0.3\\cdot\\frac{\\lvert e_i^{carbone} - \\hat{e_i}^{carbone} \\rvert}{e_i^{carbone}}}$\n",
        "</center>\n",
        "\n",
        "<br>\n",
        "Where :\n",
        "\n",
        "- $IoU_i$ is the Intersection Over Union for the ith image\n",
        "- $e_i^{carbone}$ the real carbon emission of the ith vehicle\n",
        "- $\\hat{e_i}^{carbone}$ the predicted carbon emission of the ith vehicle\n",
        "\n",
        "<p style=\"text-align: justify\">Given four predicted coordinates $(\\hat{x}^{min}_{i},\\hat{x}^{max}_{i},\\hat{y}^{min}_{i},\\hat{y}^{max}_{i})$ and the four true coordinates $(x^{min}_{i}, x^{max}_{i}, y^{min}_{i}, y^{max}_{i})$ of the box locating a vehicle in an image, here are the steps to calculate the $IoU$.</p>\n",
        "\n",
        "<p style=\"text-align: justify\">First we calculate the top left and the bottom right corner of the intersection :</p>\n",
        "<br>\n",
        "<center>\n",
        "    $x_{inter_{1}} = max(x^{min}_{i},\\hat{x}^{min}_{i})$\n",
        "</center>\n",
        "<center>\n",
        "    $x_{inter_{2}} = min(x^{max}_{i},\\hat{x}^{max}_{i})$\n",
        "</center>\n",
        "<center>\n",
        "    $y_{inter_{1}} = max(y^{min}_{i},\\hat{y}^{min}_{i})$\n",
        "</center>\n",
        "<center>\n",
        "    $y_{inter_{2}} = min(y^{max}_{i},\\hat{y}^{max}_{i})$\n",
        "</center>\n",
        "\n",
        "<p style=\"text-align: justify\">Then we calculate the area of the intersection :</p>\n",
        "<center>\n",
        "    $width\\_inter = x_{inter_{1}} - x_{inter_{2}}$\n",
        "</center>\n",
        "<center>\n",
        "    $height\\_inter = y_{inter_{1}} - y_{inter_{2}}$\n",
        "</center>\n",
        "<p style=\"text-align: justify\"></p>\n",
        "<center>\n",
        "    $area\\_inter = height\\_inter\\cdot{width\\_inter}$\n",
        "</center>\n",
        "\n",
        "<p style=\"text-align: justify\">Then we calculate the area of each box :</p>\n",
        "<center>\n",
        "    $width\\_box1 = \\hat{x}^{max}_{i}-\\hat{x}^{min}_{i}$\n",
        "</center>\n",
        "<center>\n",
        "    $height\\_box1 = \\hat{y}^{max}_{i}-\\hat{y}^{min}_{i}$\n",
        "</center>\n",
        "<center>\n",
        "    $width\\_box2 = x^{max}_{i}-x^{min}_{i}$\n",
        "</center>\n",
        "<center>\n",
        "    $height\\_box2 = y^{max}_{i}-y^{min}_{i}$\n",
        "</center>\n",
        "<p style=\"text-align: justify\"></p>\n",
        "<center>\n",
        "    $area\\_box1 = width\\_box1\\cdot{height\\_box1}$\n",
        "</center>\n",
        "<center>\n",
        "    $area\\_box2 = width\\_box2\\cdot{height\\_box2}$\n",
        "</center>\n",
        "\n",
        "<p style=\"text-align: justify\">Finally we calculate the $IoU$:</p>\n",
        "<center>\n",
        "    $area\\_union = area\\_box1 + area\\_box2 - area\\_inter$\n",
        "</center>\n",
        "<p style=\"text-align: justify\"></p>\n",
        "<center>\n",
        "    $IoU = \\frac{area\\_inter}{area\\_union}$\n",
        "</center>\n",
        "\n",
        "<br>\n",
        "<p style=\"font-size:30px;color:#0070AD\">III. Data Description </p>\n",
        "<br>\n",
        "\n",
        "<p style=\"text-align: justify\">The client has provided you with different data to answer the problem :</p>\n",
        "\n",
        "<ul>\n",
        "  <li><b>2350</b> images contained in the folder train.</li>\n",
        "    <li>a csv file named <b>_annotation.csv</b> in the folder <b>train_annotation</b> with several variables :</li>\n",
        "    <ul>\n",
        "        <li><b>im_name</b>: Image name.</li>\n",
        "        <li><b>x_min</b>: The x coordinate of the top left corner of the box locating the vehicle.</li>\n",
        "        <li><b>x_max</b>: The x coordinate of the bottom right corner of the box locating the vehicle.</li>\n",
        "        <li><b>y_min</b>: The y coordinate of the top left corner of the box locating the vehicle.</li>\n",
        "        <li><b>y_max</b>: The y coordinate of the bottom right corner of the box locating the vehicle.</li>\n",
        "        <li><b>class</b>: What is the object inside the box.</li>\n",
        "        <li><b>models</b>: the car model. </li>\n",
        "    </ul>\n",
        "        <li>an excel file named <b>car_models_footprint.csv</b> contained in the folder <b>datasets</b> with information on carbon emissions of each car model available.</li>\n",
        "    <br>\n",
        "    Feel free to add any open source data that you think is relevant to the challenge.\n",
        "\n",
        "<br>\n",
        "<p style=\"font-size:30px;color:#0070AD\">IV. Deliverables </p>\n",
        "<br>\n",
        "\n",
        "<p style=\"text-align: justify\">As part of this challenge, the client expects two deliverables from you :\n",
        "    <ul>\n",
        "        <li>A pitch presenting your solution, its advantages, its feasibility.</li>\n",
        "        <li>Your predictions on the test set saved in a `.csv` format and submitted (see the last section of the welcoming notebook).</li>\n",
        "    </ul>\n",
        "</p>\n",
        "\n",
        "<br>\n",
        "<p style=\"font-size:30px;color:#0070AD\">V. Challenge evaluation </p>\n",
        "<br>\n",
        "\n",
        "<p style=\"text-align: justify\">Based on your deliverables, you are evaluated on 3 final criteria by a jury :</p>\n",
        "<ul>\n",
        "    <li><b>Model Performance</b>: The solution delivers a satisfactory score for the metric used.</li>\n",
        "    <li><b>Business</b>: Your ability to surprise the jury with the format and content of your \"business\" presentation and its application in a professional context (a clear and synthetic oral presentation, which creates the \"Wow effect\", is expected). A clear and well-developped use case.</li>\n",
        "    <li><b>Scientific approach</b>: Clarity, rigor, creativity, justification and relevance of your scientific approach.</li>\n",
        "</ul>\n",
        "<br>\n",
        "<p style=\"text-align: justify\">To ask your questions, you have two ways to reach us :\n",
        "\n",
        "- **Coaching sessions** We provide coaching sessions (30 minutes) with our experts to answer your questions. Feel free to book a time slot by providing the name of the team and the email adress of a member via the **Doodle link** (to be completed).\n",
        "- **Q&A Google Docs** To ask questions and get answers back in an asynchronous manner. You will find the file in the GGDrive of the event ([here](https://docs.google.com/document/d/16go1QyypnCVuSCqKtM3SChoo1ex5UkIXP0MSD6BRoSk/edit?usp=sharing)).</p>\n",
        "<br>\n",
        "<p style=\"font-size:30px;color:#0070AD\">VI. Benchmark</p>\n",
        "<br>\n",
        "<p style=\"text-align: justify\"> A first naive method to predict the different bounded box is to estimate it using the average of each variable <b>x_min</b>, <b>y_min</b>, <b>x_max</b> and <b>y_max</b> measured for each bounded box, this will serve as a benchmark for the bounded box predictions. Then, we simply randomly assign the CO2 consumption of car models for each image.</p>\n",
        "\n",
        "<p style=\"text-align: justify\"> The proposed solution does not constitute a solution to be adopted to solve the challenge. It is presented to show you an example of a submission that meets the requirements.</p>"
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      "source": [
        "<p style=\"font-size:20px;color:#12ABDB;text-indent:2em\">A. Libraries import</p>"
      ]
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      "source": [
        "import matplotlib.patches as patches\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from PIL import Image\n",
        "from shapely.geometry import Polygon\n",
        "from sklearn.model_selection import train_test_split\n",
        "import os\n",
        "from tensorflow.keras.preprocessing.image import img_to_array, load_img"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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        "id": "tZr7H4fj9nbM",
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      "id": "tZr7H4fj9nbM",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# move to the challenge folder\n",
        "os.chdir(\"/content/drive/MyDrive/data_challenge_mines\")"
      ],
      "metadata": {
        "id": "OYSft-At9xyJ"
      },
      "id": "OYSft-At9xyJ",
      "execution_count": null,
      "outputs": []
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      "source": [
        "<p style=\"font-size:20px;color:#12ABDB;text-indent:2em\">B. Loading images</p>"
      ]
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          "height": 375
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        "id": "2fc9d898",
        "outputId": "2e50ca79-101d-49da-e69b-b7baee355a81"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "path_annotrain = \"datasets_train/train_annotation/_annotation.csv\"\n",
        "train_annotation = pd.read_csv(path_annotrain)\n",
        "bbox_train_full = train_annotation.loc[\n",
        "    :, [\"im_name\", \"x_min\", \"y_min\", \"x_max\", \"y_max\"]\n",
        "]\n",
        "im_test = bbox_train_full.iloc[0, 0]\n",
        "x1, y1, x2, y2 = bbox_train_full.loc[bbox_train_full[\"im_name\"] == im_test].iloc[0, 1:]\n",
        "\n",
        "\n",
        "def load_plot(im_name, x1, y1, x2, y2):\n",
        "    \"\"\"\n",
        "    Load an image, display it, and draw a rectangle on it.\n",
        "\n",
        "    Args:\n",
        "        im_name (str): The name of the image file to load.\n",
        "        x1 (int): The x-coordinate of the top-left corner of the rectangle.\n",
        "        y1 (int): The y-coordinate of the top-left corner of the rectangle.\n",
        "        x2 (int): The x-coordinate of the bottom-right corner of the rectangle.\n",
        "        y2 (int): The y-coordinate of the bottom-right corner of the rectangle.\n",
        "\n",
        "    Returns:\n",
        "        Nothing.\n",
        "    \"\"\"\n",
        "    im = np.asanyarray(Image.open(f\"datasets_train/train/{im_name}\"))\n",
        "\n",
        "    # Display the image\n",
        "    plt.imshow(im)\n",
        "\n",
        "    # Get the current reference\n",
        "    ax = plt.gca()\n",
        "\n",
        "    # Create a Rectangle patch\n",
        "    rect = patches.Rectangle(\n",
        "        (x1, y1),\n",
        "        x2 - x1,\n",
        "        y2 - y1,\n",
        "        linewidth=1,\n",
        "        edgecolor=\"r\",\n",
        "        facecolor=\"none\",\n",
        "    )\n",
        "\n",
        "    # Add the patch to the Axes\n",
        "    ax.add_patch(rect)\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "load_plot(im_test, x1, y1, x2, y2)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b34b452e",
      "metadata": {
        "deletable": false,
        "editable": false,
        "run_control": {
          "frozen": true
        },
        "id": "b34b452e"
      },
      "source": [
        "<p style=\"font-size:20px;color:#12ABDB;text-indent:2em\">C. Splitting the Data into Train & Eval Set</p>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "42d5fed5",
      "metadata": {
        "deletable": false,
        "editable": false,
        "id": "42d5fed5"
      },
      "outputs": [],
      "source": [
        "# The split is 80% - 20% between Train and Eval (test_size=0.2)\n",
        "bbox_train, bbox_eval = train_test_split(\n",
        "    bbox_train_full, test_size=0.2, random_state=1234\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f490d770",
      "metadata": {
        "deletable": false,
        "editable": false,
        "run_control": {
          "frozen": true
        },
        "id": "f490d770"
      },
      "source": [
        "<p style=\"font-size:20px;color:#12ABDB;text-indent:2em\">D. Data Preprocessing</p>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "442b7ab6",
      "metadata": {
        "deletable": false,
        "editable": false,
        "id": "442b7ab6"
      },
      "outputs": [],
      "source": [
        "def normalize_images_and_boxes(bbox_data, target_size, image_directory):\n",
        "    \"\"\"\n",
        "    Normalize the size of images and bounding boxes.\n",
        "\n",
        "    Args:\n",
        "    bbox_data (pandas.DataFrame): A DataFrame containing information about images and bounding boxes. It should have the following columns:\n",
        "        - image_name: Name of the image file.\n",
        "        - x1: X-coordinate of the top-left corner of the bounding box.\n",
        "        - y1: Y-coordinate of the top-left corner of the bounding box.\n",
        "        - x2: X-coordinate of the bottom-right corner of the bounding box.\n",
        "        - y2: Y-coordinate of the bottom-right corner of the bounding box.\n",
        "    target_size (tuple): A tuple (width, height) specifying the desired size to which images should be resized and normalized.\n",
        "    image_directory (str): The directory path where the image files are located.\n",
        "\n",
        "    Returns:\n",
        "    numpy.ndarray: A numpy array containing the preprocessed images.\n",
        "\n",
        "    numpy.ndarray: A numpy array containing the target labels, i.e., the scaled bounding boxes represented as [x1_scaled, y1_scaled, x2_scaled, y2_scaled].\n",
        "    \"\"\"\n",
        "\n",
        "    images = []  # List to store the preprocessed images\n",
        "    labels = []  # List to store the target labels (scaled bounding boxes)\n",
        "\n",
        "    for index, row in bbox_data.iterrows():\n",
        "        image_name, x1, y1, x2, y2 = row\n",
        "\n",
        "        # Load and normalize the image\n",
        "        image = load_img(f\"{image_directory}/{image_name}\", target_size=target_size)\n",
        "        image = img_to_array(image) / 255.0\n",
        "        images.append(image)\n",
        "\n",
        "        # Calculate scaling factors for bounding box coordinates\n",
        "        original_width, original_height = load_img(\n",
        "            f\"{image_directory}/{image_name}\"\n",
        "        ).size\n",
        "        scale_x = target_size[0] / original_width\n",
        "        scale_y = target_size[1] / original_height\n",
        "\n",
        "        # Resize and scale bounding box coordinates\n",
        "        x1_scaled = x1 * scale_x\n",
        "        y1_scaled = y1 * scale_y\n",
        "        x2_scaled = x2 * scale_x\n",
        "        y2_scaled = y2 * scale_y\n",
        "\n",
        "        label = [x1_scaled, y1_scaled, x2_scaled, y2_scaled]\n",
        "        labels.append(label)\n",
        "\n",
        "    return np.array(images), np.array(labels)\n",
        "\n",
        "\n",
        "# Define other preprocessing functions here:\n",
        "def other_preprocessing(data):\n",
        "    # Write your code here\n",
        "    return data\n",
        "\n",
        "\n",
        "# Union all the preprocessing functions\n",
        "def preprocess_data(data, preprocessing_functions):\n",
        "    \"\"\"\n",
        "    Preprocesses the input data using a list of preprocessing functions.\n",
        "\n",
        "    Args:\n",
        "        data (Any): The input data to be preprocessed.\n",
        "        preprocessing_functions (list): A list of functions to apply to the input data.\n",
        "\n",
        "    Returns:\n",
        "        Any: The preprocessed data after applying all specified preprocessing functions.\n",
        "    \"\"\"\n",
        "    for func in preprocessing_functions:\n",
        "        data = func(data)\n",
        "    return data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "548aa09a",
      "metadata": {
        "deletable": false,
        "editable": false,
        "run_control": {
          "frozen": true
        },
        "id": "548aa09a"
      },
      "source": [
        "<p style=\"font-size:20px;color:#12ABDB;text-indent:2em\">E. Metric to be recommended to use</p>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "daa2a433",
      "metadata": {
        "deletable": false,
        "editable": false,
        "id": "daa2a433"
      },
      "outputs": [],
      "source": [
        "def local_IoU(\n",
        "    xmin_pred_i,\n",
        "    xmax_pred_i,\n",
        "    ymin_pred_i,\n",
        "    ymax_pred_i,\n",
        "    xmin_true_i,\n",
        "    xmax_true_i,\n",
        "    ymin_true_i,\n",
        "    ymax_true_i,\n",
        "):\n",
        "    \"\"\"This function calculates the IoU for the image i.\n",
        "\n",
        "    Args:\n",
        "        xmin_pred_i: Value of the prediction min x-axis.\n",
        "        xmax_pred_i: Value of the prediction max x-axis.\n",
        "        ymin_pred_i: Value of the prediction min y-axis.\n",
        "        ymax_pred_i: Value of the prediction max y-axis.\n",
        "        xmin_true_i: Value of the true min x-axis.\n",
        "        xmax_true_i: Value of the true max x-axis.\n",
        "        ymin_true_i: Value of the true min y-axis.\n",
        "        ymax_true_i: Value of the true max y-axis.\n",
        "\n",
        "    Returns:\n",
        "        The return value is the intersection over union.\n",
        "\n",
        "    \"\"\"\n",
        "    if (xmin_true_i, xmax_true_i, ymin_true_i, ymax_true_i) == (0, 0, 0, 0):\n",
        "        if (xmin_pred_i, xmax_pred_i, ymin_pred_i, ymax_pred_i) == (\n",
        "            0,\n",
        "            0,\n",
        "            0,\n",
        "            0,\n",
        "        ):\n",
        "            return 1\n",
        "\n",
        "        else:\n",
        "            return 0\n",
        "\n",
        "    else:\n",
        "        box_pred_i = [\n",
        "            [xmin_pred_i, ymin_pred_i],\n",
        "            [xmax_pred_i, ymin_pred_i],\n",
        "            [xmax_pred_i, ymax_pred_i],\n",
        "            [xmin_pred_i, ymax_pred_i],\n",
        "        ]\n",
        "        box_true_i = [\n",
        "            [xmin_true_i, ymin_true_i],\n",
        "            [xmax_true_i, ymin_true_i],\n",
        "            [xmax_true_i, ymax_true_i],\n",
        "            [xmin_true_i, ymax_true_i],\n",
        "        ]\n",
        "        poly_1 = Polygon(box_pred_i)\n",
        "        poly_2 = Polygon(box_true_i)\n",
        "        try:\n",
        "            iou = poly_1.intersection(poly_2).area / poly_1.union(poly_2).area\n",
        "            return iou\n",
        "        except:\n",
        "            return 0\n",
        "\n",
        "\n",
        "def local_MAPE(e_true_i, e_pred_i):\n",
        "    \"\"\"\n",
        "    Calculate the Mean Absolute Percentage Error (MAPE) for the image i.\n",
        "\n",
        "    Args:\n",
        "    e_true_i (float): The true values representing the true gas emission.\n",
        "    e_pred_i (float): The predicted values representing the predicted gas emission.\n",
        "\n",
        "    Returns:\n",
        "    float: MAPE score between 0 and 1.\n",
        "    \"\"\"\n",
        "    if e_true_i != 0:\n",
        "        return abs(e_true_i - e_pred_i) / e_true_i\n",
        "    else:\n",
        "        if e_pred_i == 0:\n",
        "            return 1\n",
        "        else:\n",
        "            return 0\n",
        "\n",
        "\n",
        "def metrics_global(array_local_IoU, array_local_MAPE):\n",
        "    \"\"\"\n",
        "    Calculate a global metric based on local IoU and MAPE values.\n",
        "\n",
        "    Args:\n",
        "        array_local_IoU (numpy.ndarray): An array containing local IoU (Intersection over Union) values.\n",
        "        array_local_MAPE (numpy.ndarray): An array containing local MAPE (Mean Absolute Percentage Error) values.\n",
        "\n",
        "    Returns:\n",
        "        float: The global metric computed as the mean of a weighted sum of IoU and MAPE values.\n",
        "    \"\"\"\n",
        "    # Combine IoU and MAPE using a weighted sum\n",
        "    metrics = np.mean(0.7 * (1 - array_local_IoU) + 0.3 * array_local_MAPE)\n",
        "\n",
        "    return metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b4d2196d",
      "metadata": {
        "deletable": false,
        "editable": false,
        "run_control": {
          "frozen": true
        },
        "id": "b4d2196d"
      },
      "source": [
        "<p style=\"font-size:20px;color:#12ABDB;text-indent:2em\">F. Applying a naive model</p>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "94626fd5",
      "metadata": {
        "deletable": false,
        "editable": false,
        "run_control": {
          "frozen": true
        },
        "id": "94626fd5"
      },
      "source": [
        "<li> Implementation of the naive method and evaluation on an evaluation image</li>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9e5745f0",
      "metadata": {
        "deletable": false,
        "editable": false,
        "id": "9e5745f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "outputId": "4958b89d-cbd6-4456-e102-b74b0d4ce225"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "eval_pred = bbox_eval.copy()\n",
        "\n",
        "# naive predictions for the bounded box\n",
        "eval_pred[\"x_min\"] = np.mean(bbox_train[\"x_min\"])\n",
        "eval_pred[\"x_max\"] = np.mean(bbox_train[\"x_max\"])\n",
        "eval_pred[\"y_min\"] = np.mean(bbox_train[\"y_min\"])\n",
        "eval_pred[\"y_max\"] = np.mean(bbox_train[\"y_max\"])\n",
        "\n",
        "# test on the first image\n",
        "im_pred_test = eval_pred.iloc[2, 0]\n",
        "x1, y1, x2, y2 = eval_pred.loc[eval_pred[\"im_name\"] == im_pred_test].iloc[0, 1:]\n",
        "\n",
        "load_plot(im_pred_test, x1, y1, x2, y2)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "23efd684",
      "metadata": {
        "deletable": false,
        "editable": false,
        "run_control": {
          "frozen": true
        },
        "id": "23efd684"
      },
      "source": [
        "<li>Random assignment of car models</li>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fe441451",
      "metadata": {
        "deletable": false,
        "editable": false,
        "id": "fe441451"
      },
      "outputs": [],
      "source": [
        "cars_path = \"carbon_footprint/models_consumption.csv\"\n",
        "car_models = pd.read_csv(cars_path, sep=\",\")\n",
        "\n",
        "# randomly assign CO2 consumption of car models\n",
        "np.random.seed(1234)\n",
        "emission = car_models[\"Average emission of CO2\"].dropna()\n",
        "eval_pred[\"emissions\"] = eval_pred[\"x_min\"].apply(lambda x: np.random.choice(emission, 1)[0])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "eval_pred.head()"
      ],
      "metadata": {
        "id": "rkiIgEqbCgtg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "6d94849b-501a-4b3a-ef30-9629cad40d24"
      },
      "id": "rkiIgEqbCgtg",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            im_name      x_min       y_min       x_max      y_max    emissions\n",
              "862    42040181.jpg  59.357827  100.829073  572.899894  370.28541  190,6631579\n",
              "1407  615907328.jpg  59.357827  100.829073  572.899894  370.28541          225\n",
              "463   227278848.jpg  59.357827  100.829073  572.899894  370.28541  190,6631579\n",
              "685   356843520.jpg  59.357827  100.829073  572.899894  370.28541  190,6631579\n",
              "2062  893124608.jpg  59.357827  100.829073  572.899894  370.28541          225"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3a6d656c-78df-46b6-903e-e16fa03a8367\" class=\"colab-df-container\">\n",
              "    <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>im_name</th>\n",
              "      <th>x_min</th>\n",
              "      <th>y_min</th>\n",
              "      <th>x_max</th>\n",
              "      <th>y_max</th>\n",
              "      <th>emissions</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>862</th>\n",
              "      <td>42040181.jpg</td>\n",
              "      <td>59.357827</td>\n",
              "      <td>100.829073</td>\n",
              "      <td>572.899894</td>\n",
              "      <td>370.28541</td>\n",
              "      <td>190,6631579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1407</th>\n",
              "      <td>615907328.jpg</td>\n",
              "      <td>59.357827</td>\n",
              "      <td>100.829073</td>\n",
              "      <td>572.899894</td>\n",
              "      <td>370.28541</td>\n",
              "      <td>225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>463</th>\n",
              "      <td>227278848.jpg</td>\n",
              "      <td>59.357827</td>\n",
              "      <td>100.829073</td>\n",
              "      <td>572.899894</td>\n",
              "      <td>370.28541</td>\n",
              "      <td>190,6631579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>685</th>\n",
              "      <td>356843520.jpg</td>\n",
              "      <td>59.357827</td>\n",
              "      <td>100.829073</td>\n",
              "      <td>572.899894</td>\n",
              "      <td>370.28541</td>\n",
              "      <td>190,6631579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2062</th>\n",
              "      <td>893124608.jpg</td>\n",
              "      <td>59.357827</td>\n",
              "      <td>100.829073</td>\n",
              "      <td>572.899894</td>\n",
              "      <td>370.28541</td>\n",
              "      <td>225</td>\n",
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              "  </tbody>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-3a6d656c-78df-46b6-903e-e16fa03a8367 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-3a6d656c-78df-46b6-903e-e16fa03a8367');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-899124ba-1fd2-443b-a12f-99468e7b9d32')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </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",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    fill: var(--fill-color);\n",
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              "\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",
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              "\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",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    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",
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              "        document.querySelector('#df-899124ba-1fd2-443b-a12f-99468e7b9d32 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cfaecdfe",
      "metadata": {
        "deletable": false,
        "editable": false,
        "run_control": {
          "frozen": true
        },
        "id": "cfaecdfe"
      },
      "source": [
        "<p style=\"font-size:20px;color:#12ABDB;text-indent:2em\">G. Submission</p>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "When access to the tests folder is given (around 13h on Thursday) you need to generate the predictions of you model from the test images.\n",
        "- Generate a test_pred dataframe with the same structure (same columns order/names) as eval_pred above."
      ],
      "metadata": {
        "id": "m9jxhRkBC_YH"
      },
      "id": "m9jxhRkBC_YH"
    },
    {
      "cell_type": "markdown",
      "id": "72883e9a",
      "metadata": {
        "deletable": false,
        "editable": false,
        "run_control": {
          "frozen": true
        },
        "id": "72883e9a"
      },
      "source": [
        "Save it under a csv file on your drive."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "08590921",
      "metadata": {
        "id": "08590921"
      },
      "outputs": [],
      "source": [
        "# Submit the results (in CSV format)\n",
        "#eval_pred.to_csv(\"test_team2.csv\", index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8ab1fb7b",
      "metadata": {
        "id": "8ab1fb7b"
      },
      "source": [
        "Then, submit your predictions using the following form : https://forms.gle/GxtRup6nZuN85rNMA\n",
        "\n",
        "If you face any issues submitting your file, feel free to reach the organizers."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "car_models.columns"
      ],
      "metadata": {
        "id": "g49mG_sxDdCF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "359a301a-d631-410e-c560-43159e633251"
      },
      "id": "g49mG_sxDdCF",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['full name', 'Brand', 'year', 'Average urban consumption (L/100km)',\n",
              "       'Average extra-urban consumption (L/100km)',\n",
              "       'Average mixed consumption (L/100km)', 'Average emission of CO2',\n",
              "       'Average emission of CO type I'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import pandas as pd\n",
        "\n",
        "# Read the CSV file\n",
        "cars_path = \"carbon_footprint/models_consumption.csv\"\n",
        "car_models = pd.read_csv(cars_path, sep=\",\")\n",
        "\n",
        "# Remove commas and convert to float\n",
        "car_models['Average emission of CO2'] = car_models['Average emission of CO2'].str.replace(',', '.', regex=True).astype(float\n",
        "                                                                                                                       )\n",
        "car_models=car_models.sort_values(by='Average emission of CO2')\n",
        "emissions=car_models[['Average emission of CO2', 'full name']]\n",
        "\n",
        "emissions.set_index('full name')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "G2H8Kkyw2tlx",
        "outputId": "23065f95-5366-4817-98be-34c8d7dda89c"
      },
      "id": "G2H8Kkyw2tlx",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                    Average emission of CO2\n",
              "full name                                                                  \n",
              "Fisker Karma Sedan 2012                                           51.000000\n",
              "Ford Fiesta Sedan 2012                                           107.000000\n",
              "FIAT 500 Convertible 2012                                        111.000000\n",
              "Toyota Camry Sedan 2012                                          129.142857\n",
              "Toyota Sequoia SUV 2012                                          129.142857\n",
              "...                                                                     ...\n",
              "Bentley Continental GT Coupe 2007                                440.555556\n",
              "Lamborghini Reventon Coupe 2008                                  441.721312\n",
              "Rolls-Royce Phantom Sedan 2012                                   450.000000\n",
              "Rolls-Royce Phantom Drophead Coupe Convertible ...               450.000000\n",
              "AM General Hummer SUV 2000                                       650.000000\n",
              "\n",
              "[100 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
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              "      <th>full name</th>\n",
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              "      <th>Ford Fiesta Sedan 2012</th>\n",
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              "      <td>111.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Toyota Camry Sedan 2012</th>\n",
              "      <td>129.142857</td>\n",
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              "    <tr>\n",
              "      <th>Toyota Sequoia SUV 2012</th>\n",
              "      <td>129.142857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "    </tr>\n",
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              "      <th>Bentley Continental GT Coupe 2007</th>\n",
              "      <td>440.555556</td>\n",
              "    </tr>\n",
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              "      <th>Lamborghini Reventon Coupe 2008</th>\n",
              "      <td>441.721312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rolls-Royce Phantom Sedan 2012</th>\n",
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              "    </tr>\n",
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              "      <th>AM General Hummer SUV 2000</th>\n",
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              "<p>100 rows × 1 columns</p>\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        const element = document.querySelector('#df-9c0bba7b-bd82-413d-a3f4-df58bd19eb48');\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",
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              "  [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-16449653-42e0-4af2-bd68-af0e1a5b02bb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Read the CSV file\n",
        "path_annotrain = \"datasets_train/train_annotation/_annotation.csv\"\n",
        "train_annotation = pd.read_csv(path_annotrain)\n",
        "train_annotation_bymodel=train_annotation.groupby(by='models')\n",
        "train_annotation_bymodel=train_annotation_bymodel.size().sort_values(ascending=False)\n",
        "\n",
        "train_annotation_bymodel_df = pd.DataFrame(train_annotation_bymodel, columns = ['occurence'])\n",
        "train_annotation_bymodel_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "TaqLjqdVCng_",
        "outputId": "687cf376-2867-4186-e5b2-ad0cf4dc2465"
      },
      "id": "TaqLjqdVCng_",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                    occurence\n",
              "models                                                       \n",
              "Volvo XC90 SUV 2007                                        33\n",
              "Mercedes-Benz 300-Class Convertible 1993                   33\n",
              "Audi 100 Wagon 1994                                        30\n",
              "Volkswagen Beetle Hatchback 2012                           29\n",
              "Chrysler 300 SRT-8 2010                                    28\n",
              "...                                                       ...\n",
              "Rolls-Royce Phantom Drophead Coupe Convertible ...         14\n",
              "Suzuki Aerio Sedan 2007                                    14\n",
              "Ford F-450 Super Duty Crew Cab 2012                        14\n",
              "Jeep Grand Cherokee SUV 2012                               11\n",
              "BMW ActiveHybrid 5 Sedan 2012                               9\n",
              "\n",
              "[100 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7e03fb6f-f983-4517-b59b-47b7f7035303\" class=\"colab-df-container\">\n",
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              "      <th>occurence</th>\n",
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              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Volvo XC90 SUV 2007</th>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mercedes-Benz 300-Class Convertible 1993</th>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Audi 100 Wagon 1994</th>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
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              "      <th>Volkswagen Beetle Hatchback 2012</th>\n",
              "      <td>29</td>\n",
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              "    <tr>\n",
              "      <th>Chrysler 300 SRT-8 2010</th>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rolls-Royce Phantom Drophead Coupe Convertible 2012</th>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Suzuki Aerio Sedan 2007</th>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ford F-450 Super Duty Crew Cab 2012</th>\n",
              "      <td>14</td>\n",
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              "    <tr>\n",
              "      <th>Jeep Grand Cherokee SUV 2012</th>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BMW ActiveHybrid 5 Sedan 2012</th>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 1 columns</p>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7e03fb6f-f983-4517-b59b-47b7f7035303 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-7e03fb6f-f983-4517-b59b-47b7f7035303');\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",
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              "    </g>\n",
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              "\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\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",
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              "\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",
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              "    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-1eb2c137-225e-47f4-a076-9c4962ca471e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_emissions = pd.merge(emissions, train_annotation_bymodel_df, left_on='full name', right_on='models')\n",
        "\n",
        "merged_emissions.tail(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "8NBakXCYUrvV",
        "outputId": "1b60d0f7-7e3a-4f29-88ef-8feb36558408"
      },
      "id": "8NBakXCYUrvV",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Average emission of CO2  \\\n",
              "90               350.000000   \n",
              "91               358.307692   \n",
              "92               375.000000   \n",
              "93               398.000000   \n",
              "94               400.000000   \n",
              "95               440.555556   \n",
              "96               441.721312   \n",
              "97               450.000000   \n",
              "98               450.000000   \n",
              "99               650.000000   \n",
              "\n",
              "                                            full name  occurence  \n",
              "90                               Volvo 240 Sedan 1993         25  \n",
              "91                 Maybach Landaulet Convertible 2012         17  \n",
              "92                               Spyker C8 Coupe 2009         20  \n",
              "93                   Lamborghini Aventador Coupe 2012         18  \n",
              "94                           HUMMER H3T Crew Cab 2010         20  \n",
              "95                  Bentley Continental GT Coupe 2007         26  \n",
              "96                    Lamborghini Reventon Coupe 2008         19  \n",
              "97                     Rolls-Royce Phantom Sedan 2012         24  \n",
              "98  Rolls-Royce Phantom Drophead Coupe Convertible...         14  \n",
              "99                         AM General Hummer SUV 2000         21  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Average emission of CO2</th>\n",
              "      <th>full name</th>\n",
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              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>398.000000</td>\n",
              "      <td>Lamborghini Aventador Coupe 2012</td>\n",
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              "      <td>400.000000</td>\n",
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              "      <td>20</td>\n",
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              "      <th>95</th>\n",
              "      <td>440.555556</td>\n",
              "      <td>Bentley Continental GT Coupe 2007</td>\n",
              "      <td>26</td>\n",
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              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>441.721312</td>\n",
              "      <td>Lamborghini Reventon Coupe 2008</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>450.000000</td>\n",
              "      <td>Rolls-Royce Phantom Sedan 2012</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>450.000000</td>\n",
              "      <td>Rolls-Royce Phantom Drophead Coupe Convertible...</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>650.000000</td>\n",
              "      <td>AM General Hummer SUV 2000</td>\n",
              "      <td>21</td>\n",
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              "        document.querySelector('#df-9a6b0864-4afa-4ff6-80fa-3f531d860d67 button.colab-df-convert');\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9a6b0864-4afa-4ff6-80fa-3f531d860d67');\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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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "\n",
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              "  }\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",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "  }\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",
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              "      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-4c2e0b16-c4f3-4d90-be3b-ffb2b4573fe9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "byoccurence=merged_emissions['Average emission of CO2']*merged_emissions['occurence']\n",
        "#occurence=pd.merge(merged_emissions, byoccurence,on='index')\n",
        "byoccurence=pd.DataFrame(byoccurence, columns = ['total'])\n",
        "byoccurence.columns\n",
        "occurence=pd.merge(merged_emissions, byoccurence,right_index=True, left_index=True)\n",
        "occ=occurence.sort_values(by='total')\n",
        "occ\n",
        "ban1=occ[occ['Average emission of CO2']<250]\n",
        "print(1-(ban1['total'].sum()/occ['total'].sum())) #pourcentage d'emission en moins\n",
        "print(1-(ban1['occurence'].sum()/occ['occurence'].sum())) #pourcentage de suppression voiture\n",
        "s=(occ['occurence'].sum()-ban1['occurence'].sum())\n",
        "s/ban1.shape[0]\n",
        "ban2=ban1\n",
        "ban2=ban1['occurence']+int(s/ban1.shape[0])\n",
        "ban2=pd.DataFrame(ban2)\n",
        "\n",
        "inter=ban1[['Average emission of CO2', 'full name']]\n",
        "new=pd.merge(inter, ban2,right_index=True, left_index=True)\n",
        "new_tot=new['Average emission of CO2']*new['occurence']\n",
        "\n",
        "new_tot=pd.DataFrame(new_tot,columns = ['total'])\n",
        "\n",
        "final=pd.merge(new_tot, new,right_index=True, left_index=True)\n",
        "\n",
        "print(1-final['total'].sum()/occ['total'].sum()) #pourcentage d'emission en moins\n",
        "print(1-final['occurence'].sum()/occ['occurence'].sum()) #pourcentage de supp voiture"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qG0g4nPjlsPd",
        "outputId": "bf43e5f2-ef97-4704-911f-1916eb7f4578"
      },
      "id": "qG0g4nPjlsPd",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.47192227231705264\n",
            "0.31375358166189116\n",
            "0.24417423129212612\n",
            "0.017191977077363862\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(occ['total'].tail(12).sum()/occ['total'].sum())\n",
        "occ.tail(12)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "uhsaoeKapYPf",
        "outputId": "fe94f652-b39b-4453-faa2-f85977b3d439"
      },
      "id": "uhsaoeKapYPf",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.23156049671446954\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Average emission of CO2                                 full name  \\\n",
              "89               350.000000                        Audi V8 Sedan 1994   \n",
              "74               293.000000               Chevrolet Corvette ZR1 2012   \n",
              "94               400.000000                  HUMMER H3T Crew Cab 2010   \n",
              "85               339.250000  Aston Martin V8 Vantage Convertible 2012   \n",
              "96               441.721312           Lamborghini Reventon Coupe 2008   \n",
              "82               300.000000                   Land Rover LR2 SUV 2012   \n",
              "90               350.000000                      Volvo 240 Sedan 1993   \n",
              "80               300.000000                       Audi 100 Wagon 1994   \n",
              "71               275.000000  Mercedes-Benz 300-Class Convertible 1993   \n",
              "97               450.000000            Rolls-Royce Phantom Sedan 2012   \n",
              "95               440.555556         Bentley Continental GT Coupe 2007   \n",
              "99               650.000000                AM General Hummer SUV 2000   \n",
              "\n",
              "    occurence         total  \n",
              "89         22   7700.000000  \n",
              "74         27   7911.000000  \n",
              "94         20   8000.000000  \n",
              "85         24   8142.000000  \n",
              "96         19   8392.704918  \n",
              "82         28   8400.000000  \n",
              "90         25   8750.000000  \n",
              "80         30   9000.000000  \n",
              "71         33   9075.000000  \n",
              "97         24  10800.000000  \n",
              "95         26  11454.444446  \n",
              "99         21  13650.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-58d4202b-8184-4428-8f1d-6d732c727deb\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Average emission of CO2</th>\n",
              "      <th>full name</th>\n",
              "      <th>occurence</th>\n",
              "      <th>total</th>\n",
              "    </tr>\n",
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              "      <th>89</th>\n",
              "      <td>350.000000</td>\n",
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              "      <td>22</td>\n",
              "      <td>7700.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>293.000000</td>\n",
              "      <td>Chevrolet Corvette ZR1 2012</td>\n",
              "      <td>27</td>\n",
              "      <td>7911.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>400.000000</td>\n",
              "      <td>HUMMER H3T Crew Cab 2010</td>\n",
              "      <td>20</td>\n",
              "      <td>8000.000000</td>\n",
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              "      <th>85</th>\n",
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              "      <td>Aston Martin V8 Vantage Convertible 2012</td>\n",
              "      <td>24</td>\n",
              "      <td>8142.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>441.721312</td>\n",
              "      <td>Lamborghini Reventon Coupe 2008</td>\n",
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              "      <td>8392.704918</td>\n",
              "    </tr>\n",
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              "      <td>300.000000</td>\n",
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              "      <th>90</th>\n",
              "      <td>350.000000</td>\n",
              "      <td>Volvo 240 Sedan 1993</td>\n",
              "      <td>25</td>\n",
              "      <td>8750.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>300.000000</td>\n",
              "      <td>Audi 100 Wagon 1994</td>\n",
              "      <td>30</td>\n",
              "      <td>9000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>275.000000</td>\n",
              "      <td>Mercedes-Benz 300-Class Convertible 1993</td>\n",
              "      <td>33</td>\n",
              "      <td>9075.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>450.000000</td>\n",
              "      <td>Rolls-Royce Phantom Sedan 2012</td>\n",
              "      <td>24</td>\n",
              "      <td>10800.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>440.555556</td>\n",
              "      <td>Bentley Continental GT Coupe 2007</td>\n",
              "      <td>26</td>\n",
              "      <td>11454.444446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>650.000000</td>\n",
              "      <td>AM General Hummer SUV 2000</td>\n",
              "      <td>21</td>\n",
              "      <td>13650.000000</td>\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "\n",
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              "        const element = document.querySelector('#df-58d4202b-8184-4428-8f1d-6d732c727deb');\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-2cf5268d-a98b-4577-af02-39f8f137ad25\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2cf5268d-a98b-4577-af02-39f8f137ad25')\"\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-2cf5268d-a98b-4577-af02-39f8f137ad25 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "car_models['Average emission of CO2'].isna().value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9IPCfbcLCxtM",
        "outputId": "93697990-e390-4b49-8fd6-ff5077c75e6a"
      },
      "id": "9IPCfbcLCxtM",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    100\n",
              "Name: Average emission of CO2, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#car_models.sort_values(by='Average emission of CO2',ascending=True).tail(10)\n",
        "#car_models['Average emission of CO2'].iloc[85].dtypes #car_models['Average emission of CO2'].iloc[86]\n",
        "\n",
        "# Assuming car_models is your DataFrame\n",
        "data_type = car_models['Average emission of CO2'].dtypes\n",
        "print(data_type)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BL-cfl9wDYZK",
        "outputId": "6b0e4f5d-dcd9-40cc-eca8-2c3e7d0d32da"
      },
      "id": "BL-cfl9wDYZK",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OtBm0ALGErTd"
      },
      "id": "OtBm0ALGErTd",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "celltoolbar": "Aucun(e)",
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.4"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}