{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kATYAws2Gmq7"
      },
      "source": [
        "\n",
        "## Projet Fin Module - nalyse des publications scientifiques avec l'API Scopus\n",
        "##### Réalisé Par :\n",
        "\n",
        "> Zaidane najat\n",
        "\n",
        "\n",
        "> Sadok Mohammed\n",
        "\n",
        "\n",
        "> Sejjari Soufiane\n",
        "\n",
        "\n",
        "## Introduction\n",
        "\n",
        "Ce notebook présente un script Python conçu pour récupérer des informations sur les publications scientifiques à partir de l'API Scopus et pour les analyser et les visualiser.\n",
        "\n",
        "## API Elsevier\n",
        "\n",
        "L'API Elsevier fournit un accès aux données bibliographiques et aux informations sur les auteurs et les affiliations. Elle est documentée ici : https://dev.elsevier.com/api_docs.html.\n",
        "\n",
        "## Clé API\n",
        "\n",
        "La clé API utilisée pour ce projet est la suivante :\n",
        "\n",
        "0e7734ba689cfd558d5823687037e4a5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FnToPY_KBiJK",
        "outputId": "d0ebe89c-c298-4eb0-84ea-58154f740918"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: elsapy in c:\\users\\sejja\\anaconda3\\lib\\site-packages (0.5.1)\n",
            "Requirement already satisfied: requests in c:\\users\\sejja\\anaconda3\\lib\\site-packages (from elsapy) (2.29.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\sejja\\anaconda3\\lib\\site-packages (from requests->elsapy) (2.0.4)\n",
            "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\sejja\\anaconda3\\lib\\site-packages (from requests->elsapy) (3.4)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\sejja\\anaconda3\\lib\\site-packages (from requests->elsapy) (1.26.16)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\sejja\\anaconda3\\lib\\site-packages (from requests->elsapy) (2023.5.7)\n",
            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        }
      ],
      "source": [
        "pip install requests pandas matplotlib numpy seaborn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "feRW2dmWBl2d"
      },
      "outputs": [],
      "source": [
        "from elsapy.elsclient import ElsClient\n",
        "from elsapy.elsdoc import FullDoc\n",
        "from elsapy.elssearch import ElsSearch\n",
        "import requests, json\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0UP8e__GGMFm"
      },
      "source": [
        "## Récuperation et Extraction des données depuis l'API\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xV8bRhBljILR",
        "outputId": "1d7b8610-65ea-4dbd-d2e6-ec34e0839e08"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fetching data for keyword: NLP\n",
            "Fetching data for year: 2015\n",
            "Fetching data for year: 2016\n",
            "Fetching data for year: 2017\n",
            "Fetching data for year: 2018\n",
            "Fetching data for year: 2019\n",
            "Fetching data for year: 2020\n",
            "Fetching data for year: 2021\n",
            "Fetching data for year: 2022\n",
            "Fetching data for year: 2023\n",
            "Fetching data for year: 2024\n",
            "Fetching data for keyword: Artificial Intelligence\n",
            "Fetching data for year: 2015\n",
            "Fetching data for year: 2016\n",
            "Fetching data for year: 2017\n",
            "Fetching data for year: 2018\n",
            "Fetching data for year: 2019\n",
            "Fetching data for year: 2020\n",
            "Fetching data for year: 2021\n",
            "Fetching data for year: 2022\n",
            "Fetching data for year: 2023\n",
            "Fetching data for year: 2024\n",
            "Fetching data for keyword: Machine Learning\n",
            "Fetching data for year: 2015\n",
            "Fetching data for year: 2016\n",
            "Fetching data for year: 2017\n",
            "Fetching data for year: 2018\n",
            "Fetching data for year: 2019\n",
            "Fetching data for year: 2020\n",
            "Fetching data for year: 2021\n",
            "Fetching data for year: 2022\n",
            "Fetching data for year: 2023\n",
            "Fetching data for year: 2024\n",
            "Data saved to scopus_data.csv\n"
          ]
        }
      ],
      "source": [
        "import requests\n",
        "import pandas as pd\n",
        "def fetch_scopus_data(api_key, keyword, start_year, end_year, max_results=100):\n",
        "    base_url = \"https://api.elsevier.com/content/search/scopus\"\n",
        "    query = f'PUBYEAR > {start_year - 1} AND PUBYEAR < {end_year + 1} AND (TITLE(\"{keyword}\"))'\n",
        "    params = {\n",
        "        \"query\": query,\n",
        "        \"apiKey\": api_key,\n",
        "        \"count\": 25,\n",
        "        \"start\": 0\n",
        "    }\n",
        "\n",
        "    all_results = []\n",
        "    while params[\"start\"] < max_results:\n",
        "        response = requests.get(base_url, params=params)\n",
        "        if response.status_code == 200:\n",
        "            data = response.json()\n",
        "            entries = data.get('search-results', {}).get('entry', [])\n",
        "            all_results.extend(entries)\n",
        "            if len(entries) < 25:  # If less than 25 entries, we have reached the end\n",
        "                break\n",
        "            params[\"start\"] += 25\n",
        "        else:\n",
        "            print(f\"Error: {response.status_code}, {response.text}\")\n",
        "            break\n",
        "\n",
        "    return all_results\n",
        "\n",
        "def extract_important_fields(entries, keyword):\n",
        "    extracted_data = []\n",
        "\n",
        "    for entry in entries:\n",
        "        year = entry.get('prism:coverDate', '').split('-')[0]  # Extract year from coverDate\n",
        "        title = entry.get('dc:title', '')\n",
        "        authors = entry.get('dc:creator', '')\n",
        "        publication_name = entry.get('prism:publicationName', '')\n",
        "        publication_date = entry.get('prism:coverDate', '')\n",
        "        doi = entry.get('prism:doi', '')\n",
        "        citation_count = entry.get('citedby-count', '0')\n",
        "\n",
        "        extracted_data.append({\n",
        "            'Year': year,\n",
        "            'Title': title,\n",
        "            'Authors': authors,\n",
        "            'Publication Name': publication_name,\n",
        "            'Publication Date': publication_date,\n",
        "            'DOI': doi,\n",
        "            'Citation Count': citation_count,\n",
        "            'Keyword': keyword\n",
        "        })\n",
        "\n",
        "    return extracted_data\n",
        "\n",
        "def main(): # boucle sur chaque keyword et année pour récupération des données\n",
        "    api_key = \"0e7734ba689cfd558d5823687037e4a5\"\n",
        "    keywords = [\"NLP\", \"Artificial Intelligence\", \"Machine Learning\"]\n",
        "    start_year = 2015\n",
        "    end_year = 2024\n",
        "    max_results_per_year = 1000  # Maximum number of results per year per keyword\n",
        "\n",
        "    all_data = []\n",
        "\n",
        "    for keyword in keywords:\n",
        "        print(f\"Fetching data for keyword: {keyword}\")\n",
        "        for year in range(start_year, end_year + 1):\n",
        "            print(f\"Fetching data for year: {year}\")\n",
        "            entries = fetch_scopus_data(api_key, keyword, year, year, max_results_per_year)\n",
        "            if entries:\n",
        "                extracted_data = extract_important_fields(entries, keyword)\n",
        "                all_data.extend(extracted_data)\n",
        "            else:\n",
        "                print(f\"No data fetched for {keyword} in {year}\")\n",
        "\n",
        "    df = pd.DataFrame(all_data)\n",
        "    df.to_csv(\"scopus_data_All.csv\", index=False)\n",
        "    print(\"Data saved to scopus_data.csv\")\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    main()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_xd_5TSyjILS"
      },
      "source": [
        "## Analyse et Nettoyage des Données"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FqI2MgUMjILS",
        "outputId": "08d6c7de-a10e-4c8f-c0a6-b0d66bd5f116"
      },
      "outputs": [
        {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Title</th>\n",
              "      <th>Authors</th>\n",
              "      <th>Publication Name</th>\n",
              "      <th>Publication Date</th>\n",
              "      <th>DOI</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015</td>\n",
              "      <td>Scaling NLP algorithms to meet high demand</td>\n",
              "      <td>Stokes C.</td>\n",
              "      <td>Proceedings - 2015 IEEE International Conferen...</td>\n",
              "      <td>2015-12-22</td>\n",
              "      <td>10.1109/BigData.2015.7364095</td>\n",
              "      <td>1</td>\n",
              "      <td>NLP</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015</td>\n",
              "      <td>Molecular characterization and periplasmic exp...</td>\n",
              "      <td>Hamedi J.</td>\n",
              "      <td>Annals of Microbiology</td>\n",
              "      <td>2015-12-01</td>\n",
              "      <td>10.1007/s13213-015-1043-z</td>\n",
              "      <td>1</td>\n",
              "      <td>NLP</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015</td>\n",
              "      <td>Mass Spectrometry of Single GABAergic Somatic ...</td>\n",
              "      <td>Konop C.</td>\n",
              "      <td>Journal of the American Society for Mass Spect...</td>\n",
              "      <td>2015-12-01</td>\n",
              "      <td>10.1007/s13361-015-1177-z</td>\n",
              "      <td>6</td>\n",
              "      <td>NLP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015</td>\n",
              "      <td>NLP based congestive heart failure case findin...</td>\n",
              "      <td>Wang Y.</td>\n",
              "      <td>International Journal of Medical Informatics</td>\n",
              "      <td>2015-12-01</td>\n",
              "      <td>10.1016/j.ijmedinf.2015.06.007</td>\n",
              "      <td>31</td>\n",
              "      <td>NLP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015</td>\n",
              "      <td>Blending water- and nutrient-source wastewater...</td>\n",
              "      <td>Park S.</td>\n",
              "      <td>Bioresource Technology</td>\n",
              "      <td>2015-12-01</td>\n",
              "      <td>10.1016/j.biortech.2015.09.038</td>\n",
              "      <td>17</td>\n",
              "      <td>NLP</td>\n",
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            "text/plain": [
              "   Year                                              Title    Authors  \\\n",
              "0  2015         Scaling NLP algorithms to meet high demand  Stokes C.   \n",
              "1  2015  Molecular characterization and periplasmic exp...  Hamedi J.   \n",
              "2  2015  Mass Spectrometry of Single GABAergic Somatic ...   Konop C.   \n",
              "3  2015  NLP based congestive heart failure case findin...    Wang Y.   \n",
              "4  2015  Blending water- and nutrient-source wastewater...    Park S.   \n",
              "\n",
              "                                    Publication Name Publication Date  \\\n",
              "0  Proceedings - 2015 IEEE International Conferen...       2015-12-22   \n",
              "1                             Annals of Microbiology       2015-12-01   \n",
              "2  Journal of the American Society for Mass Spect...       2015-12-01   \n",
              "3       International Journal of Medical Informatics       2015-12-01   \n",
              "4                             Bioresource Technology       2015-12-01   \n",
              "\n",
              "                              DOI  Citation Count Keyword  \n",
              "0    10.1109/BigData.2015.7364095               1     NLP  \n",
              "1       10.1007/s13213-015-1043-z               1     NLP  \n",
              "2       10.1007/s13361-015-1177-z               6     NLP  \n",
              "3  10.1016/j.ijmedinf.2015.06.007              31     NLP  \n",
              "4  10.1016/j.biortech.2015.09.038              17     NLP  "
            ]
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df=pd.read_csv('scopus_data_all.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0GgLjjx6jILT",
        "outputId": "a6a9947a-fe47-449f-aeba-373e87b82043"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3068"
            ]
          },
          "execution_count": 81,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# nombre de total des valeurs manquantes\n",
        "df.isnull().sum().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I92ahAvPjILT",
        "outputId": "9711deca-62cb-4edd-9639-835cb7a41cb7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Year: 0 les valeurs manquantes\n",
            "Title: 0 les valeurs manquantes\n",
            "Authors: 564 les valeurs manquantes\n",
            "Publication Name: 4 les valeurs manquantes\n",
            "Publication Date: 0 les valeurs manquantes\n",
            "DOI: 2500 les valeurs manquantes\n",
            "Citation Count: 0 les valeurs manquantes\n",
            "Keyword: 0 les valeurs manquantes\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Nombre de valeurs manquantes par colonne'}>"
            ]
          },
          "execution_count": 82,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "for col in df.columns:\n",
        "    print(f\"{col}: {df[col].isnull().sum()} les valeurs manquantes\")\n",
        "df.isnull().sum().plot(kind='bar', title='Nombre des valeurs manquantes par colonne', figsize=(8, 5))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wcdJokLCjILU",
        "outputId": "a605f01f-4342-44ca-b283-8de2b698e41c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0 (19875, 8)\n"
          ]
        }
      ],
      "source": [
        "# replacement des valeurs manquantes par des valeurs par défaut\n",
        "df['Authors']=df['Authors'].fillna('Unknown')\n",
        "# suppression des lignes avec des valeurs manquantes\n",
        "df = df.dropna()\n",
        "print(df.isnull().sum().sum(), df.shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3jPGR4QKjILV"
      },
      "outputs": [],
      "source": [
        "df.to_csv('scopus_data_all_cleaned.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J39HzJ6gGkId"
      },
      "source": [
        "## Traitement et Visualisation des données\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6zpng1kYjILW",
        "outputId": "2c42496f-3326-4281-b178-9f754c92a691"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\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>Year</th>\n",
              "      <th>Title</th>\n",
              "      <th>Authors</th>\n",
              "      <th>Publication Name</th>\n",
              "      <th>Publication Date</th>\n",
              "      <th>DOI</th>\n",
              "      <th>Citation Count</th>\n",
              "      <th>Keyword</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015</td>\n",
              "      <td>Scaling NLP algorithms to meet high demand</td>\n",
              "      <td>Stokes C.</td>\n",
              "      <td>Proceedings - 2015 IEEE International Conferen...</td>\n",
              "      <td>2015-12-22</td>\n",
              "      <td>10.1109/BigData.2015.7364095</td>\n",
              "      <td>1</td>\n",
              "      <td>NLP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015</td>\n",
              "      <td>Molecular characterization and periplasmic exp...</td>\n",
              "      <td>Hamedi J.</td>\n",
              "      <td>Annals of Microbiology</td>\n",
              "      <td>2015-12-01</td>\n",
              "      <td>10.1007/s13213-015-1043-z</td>\n",
              "      <td>1</td>\n",
              "      <td>NLP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015</td>\n",
              "      <td>Mass Spectrometry of Single GABAergic Somatic ...</td>\n",
              "      <td>Konop C.</td>\n",
              "      <td>Journal of the American Society for Mass Spect...</td>\n",
              "      <td>2015-12-01</td>\n",
              "      <td>10.1007/s13361-015-1177-z</td>\n",
              "      <td>6</td>\n",
              "      <td>NLP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015</td>\n",
              "      <td>NLP based congestive heart failure case findin...</td>\n",
              "      <td>Wang Y.</td>\n",
              "      <td>International Journal of Medical Informatics</td>\n",
              "      <td>2015-12-01</td>\n",
              "      <td>10.1016/j.ijmedinf.2015.06.007</td>\n",
              "      <td>31</td>\n",
              "      <td>NLP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015</td>\n",
              "      <td>Blending water- and nutrient-source wastewater...</td>\n",
              "      <td>Park S.</td>\n",
              "      <td>Bioresource Technology</td>\n",
              "      <td>2015-12-01</td>\n",
              "      <td>10.1016/j.biortech.2015.09.038</td>\n",
              "      <td>17</td>\n",
              "      <td>NLP</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year                                              Title    Authors  \\\n",
              "0  2015         Scaling NLP algorithms to meet high demand  Stokes C.   \n",
              "1  2015  Molecular characterization and periplasmic exp...  Hamedi J.   \n",
              "2  2015  Mass Spectrometry of Single GABAergic Somatic ...   Konop C.   \n",
              "3  2015  NLP based congestive heart failure case findin...    Wang Y.   \n",
              "4  2015  Blending water- and nutrient-source wastewater...    Park S.   \n",
              "\n",
              "                                    Publication Name Publication Date  \\\n",
              "0  Proceedings - 2015 IEEE International Conferen...       2015-12-22   \n",
              "1                             Annals of Microbiology       2015-12-01   \n",
              "2  Journal of the American Society for Mass Spect...       2015-12-01   \n",
              "3       International Journal of Medical Informatics       2015-12-01   \n",
              "4                             Bioresource Technology       2015-12-01   \n",
              "\n",
              "                              DOI  Citation Count Keyword  \n",
              "0    10.1109/BigData.2015.7364095               1     NLP  \n",
              "1       10.1007/s13213-015-1043-z               1     NLP  \n",
              "2       10.1007/s13361-015-1177-z               6     NLP  \n",
              "3  10.1016/j.ijmedinf.2015.06.007              31     NLP  \n",
              "4  10.1016/j.biortech.2015.09.038              17     NLP  "
            ]
          },
          "execution_count": 105,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# chargement du dataset\n",
        "df=pd.read_csv('scopus_data_all_cleaned.csv')\n",
        "df.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8acI7_tMjILX"
      },
      "outputs": [],
      "source": [
        "# informations sur dataset\n",
        "df.info()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pkWLWpLoNoOf",
        "outputId": "543826cd-2880-4330-bc30-c1df39611d52"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Citation Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>19875.000000</td>\n",
              "      <td>19875.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2020.021686</td>\n",
              "      <td>21.429836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.753644</td>\n",
              "      <td>82.575112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2015.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2018.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2020.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2022.000000</td>\n",
              "      <td>16.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2024.000000</td>\n",
              "      <td>5129.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Year  Citation Count\n",
              "count  19875.000000    19875.000000\n",
              "mean    2020.021686       21.429836\n",
              "std        2.753644       82.575112\n",
              "min     2015.000000        0.000000\n",
              "25%     2018.000000        0.000000\n",
              "50%     2020.000000        4.000000\n",
              "75%     2022.000000       16.000000\n",
              "max     2024.000000     5129.000000"
            ]
          },
          "execution_count": 103,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# analyse statistique\n",
        "df.describe()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7PeYH544TENM"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Charger les données depuis le fichier CSV\n",
        "df = pd.read_csv('scopus_data_all_cleaned.csv')\n",
        "\n",
        "def analyseDonne(keyword):\n",
        "    # Filtrer les données pour le mot-clé spécifié\n",
        "    df_keyword = df[df['Keyword'] == keyword]\n",
        "\n",
        "    # Afficher les premières lignes du dataframe\n",
        "    print(f\"Aperçu des données pour {keyword}:\")\n",
        "    print(df_keyword.head(1))\n",
        "\n",
        "    # Analyses statistiques\n",
        "    total_publications = df_keyword.shape[0]\n",
        "    total_citations = df_keyword['Citation Count'].sum()\n",
        "    avg_citations_per_publication = df_keyword['Citation Count'].mean()\n",
        "    max_citations = df_keyword['Citation Count'].max()\n",
        "    top_publications = df_keyword[df_keyword['Citation Count'] == max_citations]\n",
        "\n",
        "    print(f\"\\nStatistiques pour {keyword}:\")\n",
        "    print(f\"Nombre total de publications: {total_publications}\")\n",
        "    print(f\"Nombre total de citations: {total_citations}\")\n",
        "    print(f\"Citations moyennes par publication: {avg_citations_per_publication:.2f}\")\n",
        "    print(f\"Publication(s) avec le plus de citations:\")\n",
        "    print(top_publications)\n",
        "\n",
        "    # Visualisation de la répartition des publications par année\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    sns.countplot(x='Year', data=df_keyword)\n",
        "    plt.title(f'Répartition des publications par année pour {keyword}')\n",
        "    plt.xlabel('Année')\n",
        "    plt.ylabel('Nombre de publications')\n",
        "    plt.show()\n",
        "\n",
        "    # Distribution des citations par publication\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    sns.histplot(df_keyword['Citation Count'], bins=30)\n",
        "    plt.title(f'Distribution des citations par publication pour {keyword}')\n",
        "    plt.xlabel('Nombre de citations')\n",
        "    plt.ylabel('Nombre de publications')\n",
        "    plt.show()\n",
        "\n",
        "    # Répartition des publications par auteur\n",
        "    authors_series = df_keyword['Authors'].str.split(',').explode().value_counts()\n",
        "    top_authors = authors_series.head(10)\n",
        "\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    sns.barplot(x=top_authors.values, y=top_authors.index)\n",
        "    plt.title(f'Top 10 des auteurs pour {keyword}')\n",
        "    plt.xlabel('Nombre de publications')\n",
        "    plt.ylabel('Auteurs')\n",
        "    plt.show()\n",
        "\n",
        "    # Calcul de la corrélation entre le nombre de citations et les années de publication\n",
        "    correlation = df_keyword[['Year', 'Citation Count']].corr().iloc[0, 1]\n",
        "    print(f\"\\nCorrélation entre le nombre de citations et les années de publication pour {keyword}: {correlation:.2f}\")\n",
        "\n",
        "    # Analyse des publications avec le plus de citations\n",
        "    print(f\"\\nPublications avec le plus de citations pour {keyword}:\")\n",
        "    print(df_keyword[df_keyword['Citation Count'] > avg_citations_per_publication])\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XA6mPBFgjILY",
        "outputId": "3fe62dfc-f6d2-4ce8-e891-8de9e479ba2b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Aperçu des données pour NLP:\n",
            "   Year                                              Title    Authors  \\\n",
            "0  2015         Scaling NLP algorithms to meet high demand  Stokes C.   \n",
            "1  2015  Molecular characterization and periplasmic exp...  Hamedi J.   \n",
            "2  2015  Mass Spectrometry of Single GABAergic Somatic ...   Konop C.   \n",
            "3  2015  NLP based congestive heart failure case findin...    Wang Y.   \n",
            "4  2015  Blending water- and nutrient-source wastewater...    Park S.   \n",
            "\n",
            "                                    Publication Name Publication Date  \\\n",
            "0  Proceedings - 2015 IEEE International Conferen...       2015-12-22   \n",
            "1                             Annals of Microbiology       2015-12-01   \n",
            "2  Journal of the American Society for Mass Spect...       2015-12-01   \n",
            "3       International Journal of Medical Informatics       2015-12-01   \n",
            "4                             Bioresource Technology       2015-12-01   \n",
            "\n",
            "                              DOI  Citation Count Keyword  \n",
            "0    10.1109/BigData.2015.7364095               1     NLP  \n",
            "1       10.1007/s13213-015-1043-z               1     NLP  \n",
            "2       10.1007/s13361-015-1177-z               6     NLP  \n",
            "3  10.1016/j.ijmedinf.2015.06.007              31     NLP  \n",
            "4  10.1016/j.biortech.2015.09.038              17     NLP  \n",
            "\n",
            "Statistiques pour NLP:\n",
            "Nombre total de publications: 2315\n",
            "Nombre total de citations: 18946\n",
            "Citations moyennes par publication: 8.18\n",
            "Publication(s) avec le plus de citations:\n",
            "     Year                                              Title   Authors  \\\n",
            "169  2017  Discovery of nitrate-CPK-NLP signalling in cen...  Liu K.H.   \n",
            "\n",
            "    Publication Name Publication Date                  DOI  Citation Count  \\\n",
            "169           Nature       2017-05-18  10.1038/nature22077             400   \n",
            "\n",
            "    Keyword  \n",
            "169     NLP  \n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Corrélation entre le nombre de citations et les années de publication pour NLP: -0.30\n",
            "\n",
            "Publications avec le plus de citations pour NLP:\n",
            "      Year                                              Title  \\\n",
            "3     2015  NLP based congestive heart failure case findin...   \n",
            "4     2015  Blending water- and nutrient-source wastewater...   \n",
            "6     2015  Change impact analysis for Natural Language re...   \n",
            "10    2015  An RLP23-SOBIR1-BAK1 complex mediates NLP-trig...   \n",
            "13    2015  Integrating word embeddings and traditional NL...   \n",
            "...    ...                                                ...   \n",
            "2034  2023  A Machine Learning Approach for the NLP-Based ...   \n",
            "2035  2023  A Framework for Smart Home System with Voice C...   \n",
            "2036  2023  Knowledge graph enrichment from clinical narra...   \n",
            "2055  2023  Studying Online Travel Reviews related to tour...   \n",
            "2314  2024  Smarter people analytics with organizational t...   \n",
            "\n",
            "                    Authors  \\\n",
            "3                   Wang Y.   \n",
            "4                   Park S.   \n",
            "6                  Arora C.   \n",
            "10                Albert I.   \n",
            "13                  Zhao J.   \n",
            "...                     ...   \n",
            "2034           Silvestri S.   \n",
            "2035               Iliev Y.   \n",
            "2036             Thukral A.   \n",
            "2055  Guerrero-Rodriguez R.   \n",
            "2314                 Guo F.   \n",
            "\n",
            "                                       Publication Name Publication Date  \\\n",
            "3          International Journal of Medical Informatics       2015-12-01   \n",
            "4                                Bioresource Technology       2015-12-01   \n",
            "6     2015 IEEE 23rd International Requirements Engi...       2015-11-04   \n",
            "10                                        Nature Plants       2015-10-05   \n",
            "13    Proceedings of the International Joint Confere...       2015-09-28   \n",
            "...                                                 ...              ...   \n",
            "2034                                            Sensors       2023-01-01   \n",
            "2035                          Electronics (Switzerland)       2023-01-01   \n",
            "2036  International Journal of Information Technolog...       2023-01-01   \n",
            "2055                          Current Issues in Tourism       2023-01-01   \n",
            "2314                  Human Resource Management Journal       2024-01-01   \n",
            "\n",
            "                                 DOI  Citation Count Keyword  \n",
            "3     10.1016/j.ijmedinf.2015.06.007              31     NLP  \n",
            "4     10.1016/j.biortech.2015.09.038              17     NLP  \n",
            "6            10.1109/RE.2015.7320403              54     NLP  \n",
            "10          10.1038/nplants.2015.140             307     NLP  \n",
            "13        10.1109/IJCNN.2015.7280462               9     NLP  \n",
            "...                              ...             ...     ...  \n",
            "2034               10.3390/s23020651              20     NLP  \n",
            "2035     10.3390/electronics12010116              12     NLP  \n",
            "2036      10.1007/s41870-022-01145-y              12     NLP  \n",
            "2055   10.1080/13683500.2021.2007227              31     NLP  \n",
            "2314         10.1111/1748-8583.12426              11     NLP  \n",
            "\n",
            "[491 rows x 8 columns]\n",
            "Aperçu des données pour Artificial Intelligence:\n",
            "      Year                                              Title        Authors  \\\n",
            "2315  2015  Simulation and prediction for energy dissipate...  Abdeen M.A.M.   \n",
            "2316  2015  Recent advances on artificial intelligence and...       Abbas N.   \n",
            "2317  2015  Artificial intelligence for detecting preterm ...     Idowu I.O.   \n",
            "2318  2015  Software sensor for potable water quality thro...       Desai N.   \n",
            "2319  2015  AMEG: Adaptive mechanism for educational games...    Hamdaoui N.   \n",
            "\n",
            "                                       Publication Name Publication Date  \\\n",
            "2315                                 Cogent Engineering       2015-12-31   \n",
            "2316  Eurasip Journal on Wireless Communications and...       2015-12-26   \n",
            "2317  Proceedings - 15th IEEE International Conferen...       2015-12-22   \n",
            "2318  Proceedings - 2015 IEEE International Conferen...       2015-12-16   \n",
            "2319  2015 10th International Conference on Intellig...       2015-12-15   \n",
            "\n",
            "                                      DOI  Citation Count  \\\n",
            "2315        10.1080/23311916.2015.1018705               4   \n",
            "2316            10.1186/s13638-015-0381-7             108   \n",
            "2317  10.1109/CIT/IUCC/DASC/PICOM.2015.31              35   \n",
            "2318            10.1109/TIAR.2015.7358559               4   \n",
            "2319            10.1109/SITA.2015.7358424               9   \n",
            "\n",
            "                      Keyword  \n",
            "2315  Artificial Intelligence  \n",
            "2316  Artificial Intelligence  \n",
            "2317  Artificial Intelligence  \n",
            "2318  Artificial Intelligence  \n",
            "2319  Artificial Intelligence  \n",
            "\n",
            "Statistiques pour Artificial Intelligence:\n",
            "Nombre total de publications: 7891\n",
            "Nombre total de citations: 172851\n",
            "Citations moyennes par publication: 21.90\n",
            "Publication(s) avec le plus de citations:\n",
            "      Year                                              Title   Authors  \\\n",
            "4047  2018  Peeking Inside the Black-Box: A Survey on Expl...  Adadi A.   \n",
            "\n",
            "     Publication Name Publication Date                          DOI  \\\n",
            "4047      IEEE Access       2018-09-14  10.1109/ACCESS.2018.2870052   \n",
            "\n",
            "      Citation Count                  Keyword  \n",
            "4047            2970  Artificial Intelligence  \n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Corrélation entre le nombre de citations et les années de publication pour Artificial Intelligence: -0.17\n",
            "\n",
            "Publications avec le plus de citations pour Artificial Intelligence:\n",
            "       Year                                              Title  \\\n",
            "2316   2015  Recent advances on artificial intelligence and...   \n",
            "2317   2015  Artificial intelligence for detecting preterm ...   \n",
            "2322   2015  A review of intelligent driving style analysis...   \n",
            "2325   2015  Research priorities for robust and beneficial ...   \n",
            "2326   2015  Control strategies and artificial intelligence...   \n",
            "...     ...                                                ...   \n",
            "9144   2023  Revolutionizing healthcare: the role of artifi...   \n",
            "9172   2023  Best humans still outperform artificial intell...   \n",
            "9184   2023  Considerations for addressing bias in artifici...   \n",
            "10095  2024  Exploring the implementation of artificial int...   \n",
            "10096  2024  Leaders, practitioners and scientists' awarene...   \n",
            "\n",
            "              Authors                                   Publication Name  \\\n",
            "2316         Abbas N.  Eurasip Journal on Wireless Communications and...   \n",
            "2317       Idowu I.O.  Proceedings - 15th IEEE International Conferen...   \n",
            "2322   Meiring G.A.M.                              Sensors (Switzerland)   \n",
            "2325       Russell S.                                        AI Magazine   \n",
            "2326         Novak D.                                        AI Magazine   \n",
            "...               ...                                                ...   \n",
            "9144     Alowais S.A.                              BMC Medical Education   \n",
            "9172      Koivisto M.                                 Scientific Reports   \n",
            "9184    Abràmoff M.D.                               npj Digital Medicine   \n",
            "10095      Huang Y.H.                                    Library Hi Tech   \n",
            "10096    Harisanty D.                                    Library Hi Tech   \n",
            "\n",
            "      Publication Date                                  DOI  Citation Count  \\\n",
            "2316        2015-12-26            10.1186/s13638-015-0381-7             108   \n",
            "2317        2015-12-22  10.1109/CIT/IUCC/DASC/PICOM.2015.31              35   \n",
            "2322        2015-12-04                   10.3390/s151229822             195   \n",
            "2325        2015-12-01             10.1609/aimag.v36i4.2577             379   \n",
            "2326        2015-12-01             10.1609/aimag.v36i4.2614              23   \n",
            "...                ...                                  ...             ...   \n",
            "9144        2023-12-01           10.1186/s12909-023-04698-z             110   \n",
            "9172        2023-12-01           10.1038/s41598-023-40858-3              29   \n",
            "9184        2023-12-01           10.1038/s41746-023-00913-9              23   \n",
            "10095       2024-06-04             10.1108/LHT-03-2022-0159              27   \n",
            "10096       2024-06-04             10.1108/LHT-10-2021-0356              37   \n",
            "\n",
            "                       Keyword  \n",
            "2316   Artificial Intelligence  \n",
            "2317   Artificial Intelligence  \n",
            "2322   Artificial Intelligence  \n",
            "2325   Artificial Intelligence  \n",
            "2326   Artificial Intelligence  \n",
            "...                        ...  \n",
            "9144   Artificial Intelligence  \n",
            "9172   Artificial Intelligence  \n",
            "9184   Artificial Intelligence  \n",
            "10095  Artificial Intelligence  \n",
            "10096  Artificial Intelligence  \n",
            "\n",
            "[1592 rows x 8 columns]\n",
            "Aperçu des données pour Machine Learning:\n",
            "       Year                                              Title        Authors  \\\n",
            "10206  2015  Computational intelligence, fuzzy systems, and...      Neruda R.   \n",
            "10207  2015  Heat event detection in dairy cows with collar...  Shahriar M.S.   \n",
            "10208  2015  A machine learning approach to find associatio...      Rahman A.   \n",
            "10209  2015  CAMELOT: A machine learning approach for Coars...      Ruff K.M.   \n",
            "10210  2015  Stepping up theoretical investigations of ultr...  Mihailescu A.   \n",
            "\n",
            "                                        Publication Name Publication Date  \\\n",
            "10206  2015 Workshop on Engineering Applications - In...       2015-12-31   \n",
            "10207                    2015 IEEE SENSORS - Proceedings       2015-12-31   \n",
            "10208                    2015 IEEE SENSORS - Proceedings       2015-12-31   \n",
            "10209                        Journal of Chemical Physics       2015-12-28   \n",
            "10210  Proceedings - 2015 8th Romania Tier 2 Federati...       2015-12-28   \n",
            "\n",
            "                               DOI  Citation Count           Keyword  \n",
            "10206     10.1109/WEA.2015.7370156               0  Machine Learning  \n",
            "10207  10.1109/ICSENS.2015.7370528               9  Machine Learning  \n",
            "10208  10.1109/ICSENS.2015.7370680               3  Machine Learning  \n",
            "10209            10.1063/1.4935066              63  Machine Learning  \n",
            "10210   10.1109/ROLCG.2015.7367424               0  Machine Learning  \n",
            "\n",
            "Statistiques pour Machine Learning:\n",
            "Nombre total de publications: 9669\n",
            "Nombre total de citations: 234121\n",
            "Citations moyennes par publication: 24.21\n",
            "Publication(s) avec le plus de citations:\n",
            "       Year                                              Title      Authors  \\\n",
            "10707  2015  Machine learning: Trends, perspectives, and pr...  Jordan M.I.   \n",
            "\n",
            "      Publication Name Publication Date                      DOI  \\\n",
            "10707          Science       2015-07-17  10.1126/science.aaa8415   \n",
            "\n",
            "       Citation Count           Keyword  \n",
            "10707            5129  Machine Learning  \n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Corrélation entre le nombre de citations et les années de publication pour Machine Learning: -0.21\n",
            "\n",
            "Publications avec le plus de citations pour Machine Learning:\n",
            "       Year                                              Title  \\\n",
            "10209  2015  CAMELOT: A machine learning approach for Coars...   \n",
            "10211  2015  A comprehensive evaluation of air pollution pr...   \n",
            "10213  2015  A global prediction of seafloor sediment poros...   \n",
            "10215  2015  Prediction of Synergism from Chemical-Genetic ...   \n",
            "10217  2015  Traffic forecasting in complex urban networks:...   \n",
            "...     ...                                                ...   \n",
            "17758  2022  Prediction of type 2 diabetes using genome-wid...   \n",
            "17766  2022  A Review of Machine Learning for Near-Infrared...   \n",
            "17874  2022  Sepsis biomarkers and diagnostic tools with a ...   \n",
            "17904  2022  Dementia Detection from Speech Using Machine L...   \n",
            "18602  2023  Hand medical monitoring system based on machin...   \n",
            "\n",
            "               Authors                                   Publication Name  \\\n",
            "10209        Ruff K.M.                        Journal of Chemical Physics   \n",
            "10211            Xi X.  10th IEEE Int. Conf. on Service Operations and...   \n",
            "10213        Martin K.                       Geophysical Research Letters   \n",
            "10215    Wildenhain J.                                       Cell Systems   \n",
            "10217  Schimbinschi F.  Proceedings - 2015 IEEE International Conferen...   \n",
            "...                ...                                                ...   \n",
            "17758        Hahn S.J.                                       eBioMedicine   \n",
            "17766         Zhang W.                                            Sensors   \n",
            "17874    Komorowski M.                                       eBioMedicine   \n",
            "17904       Kumar M.R.                                            Sensors   \n",
            "18602            Yu M.                  Personal and Ubiquitous Computing   \n",
            "\n",
            "      Publication Date                           DOI  Citation Count  \\\n",
            "10209       2015-12-28             10.1063/1.4935066              63   \n",
            "10211       2015-12-28     10.1109/SOLI.2015.7367615              67   \n",
            "10213       2015-12-28          10.1002/2015GL065279              39   \n",
            "10215       2015-12-23    10.1016/j.cels.2015.12.003              84   \n",
            "10217       2015-12-22  10.1109/BigData.2015.7363854              41   \n",
            "...                ...                           ...             ...   \n",
            "17758       2022-12-01   10.1016/j.ebiom.2022.104383              25   \n",
            "17766       2022-12-01             10.3390/s22249764              32   \n",
            "17874       2022-12-01   10.1016/j.ebiom.2022.104394              39   \n",
            "17904       2022-12-01             10.3390/s22239311              27   \n",
            "18602       2023-12-01    10.1007/s00779-019-01285-2              52   \n",
            "\n",
            "                Keyword  \n",
            "10209  Machine Learning  \n",
            "10211  Machine Learning  \n",
            "10213  Machine Learning  \n",
            "10215  Machine Learning  \n",
            "10217  Machine Learning  \n",
            "...                 ...  \n",
            "17758  Machine Learning  \n",
            "17766  Machine Learning  \n",
            "17874  Machine Learning  \n",
            "17904  Machine Learning  \n",
            "18602  Machine Learning  \n",
            "\n",
            "[2035 rows x 8 columns]\n"
          ]
        }
      ],
      "source": [
        "# Analyse des données pour chaque mot-clé\n",
        "for keyword in df['Keyword'].unique():\n",
        "    analyseDonne(keyword)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dOB36iSyTxr_"
      },
      "outputs": [],
      "source": [
        "def citation_perYear(keyword):\n",
        "    # Filtrer les données pour le mot-clé spécifié\n",
        "    df_keyword = df[df['Keyword'] == keyword]\n",
        "\n",
        "    # Calculer le nombre de citations par année\n",
        "    citations_per_year = df_keyword.groupby('Year')['Citation Count'].sum().reset_index()\n",
        "\n",
        "    # Afficher le nombre de citations par année\n",
        "    print(f\"\\nNombre de citations par année pour {keyword}:\")\n",
        "    print(citations_per_year)\n",
        "\n",
        "    # Visualisation du nombre de citations par année\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    sns.barplot(x='Year', y='Citation Count', data=citations_per_year)\n",
        "    plt.title(f'Nombre de citations par année pour {keyword}')\n",
        "    plt.xlabel('Année')\n",
        "    plt.ylabel('Nombre de citations')\n",
        "    plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I_Jngjn3jILZ",
        "outputId": "500eb430-63f4-4116-bab5-3ae73585dadb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Nombre de citations par année pour NLP:\n",
            "   Year  Citation Count\n",
            "0  2015            1301\n",
            "1  2016            1615\n",
            "2  2017            3009\n",
            "3  2018            2022\n",
            "4  2019            1739\n",
            "5  2020            3081\n",
            "6  2021            2862\n",
            "7  2022            2113\n",
            "8  2023            1163\n",
            "9  2024              41\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Nombre de citations par année pour Artificial Intelligence:\n",
            "   Year  Citation Count\n",
            "0  2015           10414\n",
            "1  2016           10807\n",
            "2  2017           24206\n",
            "3  2018           42495\n",
            "4  2019           28972\n",
            "5  2020           25559\n",
            "6  2021           18735\n",
            "7  2022            9373\n",
            "8  2023            1897\n",
            "9  2024             393\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Nombre de citations par année pour Machine Learning:\n",
            "   Year  Citation Count\n",
            "0  2015           50608\n",
            "1  2016           43539\n",
            "2  2017           47782\n",
            "3  2018           35227\n",
            "4  2019           26780\n",
            "5  2020           11871\n",
            "6  2021           11064\n",
            "7  2022            5817\n",
            "8  2023            1038\n",
            "9  2024             395\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Nombre de citations par année pour chaque mot-clé\n",
        "for keyword in df['Keyword'].unique():\n",
        "    citation_perYear(keyword)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I2W1a_qpUWaz",
        "outputId": "aeb2ed2f-e55b-48b8-cf89-0ab46b14d329"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Coefficients du modèle de régression :\n",
            "Intercept : 11865.57\n",
            "Pente : -5.86\n",
            "Corrélation : -0.97\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# La régression linéaire et la corrélation entre le nombre de citations total et les années de publication\n",
        "df_regression = df.groupby('Year')['Citation Count'].mean().reset_index()\n",
        "# Effectuer la régression linéaire\n",
        "X = df_regression['Year'].values.reshape(-1, 1)\n",
        "y = df_regression['Citation Count'].values.reshape(-1, 1)\n",
        "regression_model = LinearRegression()\n",
        "regression_model.fit(X, y)\n",
        "# Calculer les citations prédites pour chaque année\n",
        "predicted_citations = regression_model.predict(X)\n",
        "# Calculer la corrélation entre le nombre de citations moyen et les années de publication\n",
        "correlation = np.corrcoef(df_regression['Year'], df_regression['Citation Count'])[0, 1]\n",
        "# Afficher les coefficients du modèle de régression et la corrélation\n",
        "print(\"Coefficients du modèle de régression :\")\n",
        "print(f\"Intercept : {regression_model.intercept_[0]:.2f}\")\n",
        "print(f\"Pente : {regression_model.coef_[0][0]:.2f}\")\n",
        "print(f\"Corrélation : {correlation:.2f}\")\n",
        "# Visualisation des données\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.scatter(X, y, color='b', label='Données réelles')\n",
        "plt.plot(X, predicted_citations, color='r', label='Régression linéaire')\n",
        "plt.title(f\"Régression linéaire du nombre de citations \")\n",
        "plt.xlabel('Année')\n",
        "plt.ylabel('Nombre de citations')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wkDcDjVQUs_y"
      },
      "source": [
        "Une corrélation de -0.97 est trés forte, ce qui signifie que les années de publication et le nombre de citations sont légèrement corrélés de manière négative"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9k_fdzvIjILb",
        "outputId": "5da0343c-14e9-49b6-b2a0-5efaf47a1c34"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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1119Lkv1LlNM7F8YYTZs27YFqzQwNGzbUkiVLdPLkSfsokvTn+fTx8bFPB3+nn7Ofn5+qVq2a7r4jIyP1r3/9S2PGjMlwgC1TpoxKlSqlPXv2aOzYsffzlAA8IghKAHKdjHxxZOfOnfXpp5+qS5cuOn78uCIiIrRx40aNHTtWzZo1U6NGjRz6ly5dWk2bNtW3336r2rVrq2LFig7rO3TooLlz56pZs2b6y1/+omrVqsnd3V2//vqr1q1bp5YtWzoEssxUvHhxjRo1Sm+//baOHTumpk2bKn/+/Pr999/1448/ytfX945ftipJf//737Vs2TI1aNBA77zzjnx8fPTpp586TLGdGccZNWqUvv32W9WtW1d/+9vfFBERoQsXLmjlypUaMGCAypYtm+52ERERkqRJkyapS5cucnd3V5kyZVSzZk3lz59fr7/+uoYPHy53d3fNnTtXe/bsueM+3n//fUVFRcnNzU0VKlSQh4dHmr79+/fXnDlz1Lx5c40aNUphYWFasWKFPvvsM/Xq1UulS5e+43NMz969e9WnTx+1bdtWpUqVkoeHh9auXau9e/dqyJAh99zew8NDH330kS5fvqxnnnlGmzZt0ujRoxUVFaXatWtLkp577jl5eHioY8eOGjx4sK5fv67Jkyfr/PnzTtWaFYYPH67ly5erfv36eueddxQQEKC5c+dqxYoV+uCDD+Tv7y9JKlGihLy9vTV37lyVK1dOefPmVUhIiEO4spowYYJq166t6tWra8iQISpZsqR+//13LVu2TFOnTr3jSO7UqVMVFRWlJk2aKDo6WkWKFNG5c+d08OBB7dy5U//617+y5FwAeMi4di4JAHgwt896dzfWWe+M+XOGrtdff90EBwebPHnymLCwMDN06FD7tNFWs2bNMpLM/Pnz011/8+ZN8+GHH5qKFSsaLy8vkzdvXlO2bFnTs2dPc+TIEXu/sLAw07x58zTbR0ZGOtR4p+eW3ixexhizdOlSU79+fZMvXz7j6elpwsLCTJs2bcx33313lzPzpx9++ME8++yzxtPT0wQFBZlBgwaZzz//PN3Z5h7kOCdOnDDdunUzQUFBxt3d3YSEhJh27dqZ33//3RiT/qx3xhgzdOhQExISYh577DGH575p0yZTo0YN4+PjYwoVKmR69Ohhdu7cmWYfSUlJpkePHqZQoULGZrM5PC/rrHfGGPPLL7+YTp06mQIFChh3d3dTpkwZM378eIfZ0FJrHT9+fJrnKckMHz7cGGPM77//bqKjo03ZsmWNr6+vyZs3r6lQoYKZOHGiw2x56enSpYvx9fU1e/fuNfXq1TPe3t4mICDA9OrVy1y+fNmh7zfffGN/7RUpUsQMGjTIfPvtt2leK5GRkaZ8+fJ3PW56NVjdaT/pvb737dtnnn/+eePv7288PDxMxYoV053dbt68eaZs2bLG3d3d4RzezYEDB0zbtm1NgQIF7FOpR0dH26/jO10ve/bsMe3atTOBgYHG3d3dBAUFmQYNGpgpU6bc85gAHg02YyzfoggASFfr1q21ZcsWHT9+XO7u7q4uB4+A6Oho/fvf/9bly5ddXQoAPHK49Q4A7iIpKUk7d+7Ujz/+qCVLlmjChAmEJAAAHgEEJQC4i4SEBNWsWVP58uVTz5491bdvX1eXBAAAsgG33gEAAACABV84CwAAAAAWBCUAAAAAsCAoAQAAAIBFrp/MISUlRSdPnpSfn5/9m8sBAAAAPHqMMbp06ZJCQkL02GN3HzPK9UHp5MmTCg0NdXUZAAAAAHKIEydOqGjRonftk+uDkp+fn6Q/T0a+fPlcXA0AAAAAV0lMTFRoaKg9I9xNrg9Kqbfb5cuXj6AEAAAAIEMfyWEyBwAAAACwcGlQGjdunJ555hn5+fkpMDBQL774og4fPuzQJzo6WjabzeHx7LPPuqhiAAAAAI8Clwal2NhYvfHGG9qyZYtiYmJ069YtNW7cWFeuXHHo17RpUyUkJNgf//3vf11UMQAAAIBHgUs/o7Ry5UqH5ZkzZyowMFA7duxQ3bp17e2enp4KCgrK0D6TkpKUlJRkX05MTMycYgEAAAA8MnLUZ5QuXrwoSQoICHBoX79+vQIDA1W6dGm99tprOn369B33MW7cOPn7+9sfTA0OAAAAwFk2Y4xxdRHSn1/+1LJlS50/f17ff/+9vX3BggXKmzevwsLCFBcXp2HDhunWrVvasWOHPD090+wnvRGl0NBQXbx4kVnvAAAAgEdYYmKi/P39M5QNcsz04H369NHevXu1ceNGh/b27dvb//3UU0+patWqCgsL04oVK9SqVas0+/H09Ew3QAEAAABARuWIoNS3b18tW7ZMGzZsuOc35AYHByssLExHjhzJpuoAAAAAPGpcGpSMMerbt6+WLFmi9evXKzw8/J7bnD17VidOnFBwcHA2VAgAAADgUeTSyRzeeOMNffXVV/r666/l5+enU6dO6dSpU7p27Zok6fLlyxo4cKA2b96s48ePa/369Xr++edVsGBBvfTSS64sHQAAAEAu5tLJHGw2W7rtM2fOVHR0tK5du6YXX3xRu3bt0oULFxQcHKz69evr3XffzfBsds58YAsAAABA7vXQTOZwr4zm7e2tVatWZVM1AAAAAPCnHPU9SgAAAACQExCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABg4dLvUQKA3CZ+VISrSwAeSsXe2efqEgDAASNKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgkcfVBeQmTw+a4+oSgIfSjvGdXV0CAACAA0aUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACxcGpTGjRunZ555Rn5+fgoMDNSLL76ow4cPO/QxxmjEiBEKCQmRt7e36tWrp/3797uoYgAAAACPApcGpdjYWL3xxhvasmWLYmJidOvWLTVu3FhXrlyx9/nggw80YcIE/fOf/9S2bdsUFBSk5557TpcuXXJh5QAAAAByszyuPPjKlSsdlmfOnKnAwEDt2LFDdevWlTFGH3/8sd5++221atVKkjR79mwVLlxYX3/9tXr27OmKsgEAAADkcjnqM0oXL16UJAUEBEiS4uLidOrUKTVu3Njex9PTU5GRkdq0aVO6+0hKSlJiYqLDAwAAAACckWOCkjFGAwYMUO3atfXUU09Jkk6dOiVJKly4sEPfwoUL29dZjRs3Tv7+/vZHaGho1hYOAAAAINfJMUGpT58+2rt3r+bNm5dmnc1mc1g2xqRpSzV06FBdvHjR/jhx4kSW1AsAAAAg93LpZ5RS9e3bV8uWLdOGDRtUtGhRe3tQUJCkP0eWgoOD7e2nT59OM8qUytPTU56enllbMAAAAIBczaUjSsYY9enTR4sXL9batWsVHh7usD48PFxBQUGKiYmxt924cUOxsbGqWbNmdpcLAAAA4BHh0hGlN954Q19//bX+85//yM/Pz/65I39/f3l7e8tms+nNN9/U2LFjVapUKZUqVUpjx46Vj4+POnXq5MrSAQAAAORiLg1KkydPliTVq1fPoX3mzJmKjo6WJA0ePFjXrl1T7969df78eVWvXl2rV6+Wn59fNlcLAAAA4FHh0qBkjLlnH5vNphEjRmjEiBFZXxAAAAAAKAfNegcAAAAAOQVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWGRKULpw4UJm7AYAAAAAcgSng9L777+vBQsW2JfbtWunAgUKqEiRItqzZ0+mFgcAAAAAruB0UJo6dapCQ0MlSTExMYqJidG3336rqKgoDRo0KNMLBAAAAIDslsfZDRISEuxBafny5WrXrp0aN26s4sWLq3r16pleIAAAAABkN6dHlPLnz68TJ05IklauXKlGjRpJkowxSk5OztzqAAAAAMAFnB5RatWqlTp16qRSpUrp7NmzioqKkiTt3r1bJUuWzPQCAQAAACC7OR2UJk6cqOLFi+vEiRP64IMPlDdvXkl/3pLXu3fvTC8QAAAAALKb00HJ3d1dAwcOTNP+5ptvZkY9AAAAAOByTgclSfrpp5+0fv16nT59WikpKQ7r3nnnnUwpDAAAAABcxemgNG3aNPXq1UsFCxZUUFCQbDabfZ3NZiMoAQAAAHjoOR2URo8erTFjxuivf/1rVtQDAAAAAC7n9PTg58+fV9u2bbOiFgAAAADIEZwOSm3bttXq1auzohYAAAAAyBGcvvWuZMmSGjZsmLZs2aKIiAi5u7s7rO/Xr1+mFQcAAAAAruB0UPr888+VN29excbGKjY21mGdzWYjKAEAAAB46DkdlOLi4rKiDgAAAADIMZz+jNLtjDEyxmRWLQAAAACQI9xXUJozZ44iIiLk7e0tb29vVahQQV9++WVm1wYAAAAALuH0rXcTJkzQsGHD1KdPH9WqVUvGGP3www96/fXX9ccff6h///5ZUScAAAAAZBung9Inn3yiyZMnq3Pnzva2li1bqnz58hoxYgRBCQAAAMBDz+lb7xISElSzZs007TVr1lRCQkKmFAUAAAAAruR0UCpZsqQWLlyYpn3BggUqVapUphQFAAAAAK7k9K13I0eOVPv27bVhwwbVqlVLNptNGzdu1Jo1a9INUAAAAADwsHF6RKl169baunWrChYsqKVLl2rx4sUqWLCgfvzxR7300ktZUSMAAAAAZCunR5Qk6emnn9ZXX32V2bUAAAAAQI6QoaCUmJiofPny2f99N6n9AAAAAOBhlaGglD9/fiUkJCgwMFCPP/64bDZbmj7GGNlsNiUnJ2d6kQAAAACQnTIUlNauXauAgABJ0rp16zLt4Bs2bND48eO1Y8cOJSQkaMmSJXrxxRft66OjozV79myHbapXr64tW7ZkWg0AAAAAYJWhoBQZGWn/d3h4uEJDQ9OMKhljdOLECacOfuXKFVWsWFFdu3ZV69at0+3TtGlTzZw5077s4eHh1DEAAAAAwFlOT+YQHh5uvw3vdufOnVN4eLhTt95FRUUpKirqrn08PT0VFBTkbJkAAAAAcN+cnh489bNIVpcvX5aXl1emFHW79evXKzAwUKVLl9Zrr72m06dP37V/UlKSEhMTHR4AAAAA4IwMjygNGDBAkmSz2TRs2DD5+PjY1yUnJ2vr1q2qVKlSphYXFRWltm3bKiwsTHFxcRo2bJgaNGigHTt2yNPTM91txo0bp5EjR2ZqHQAAAAAeLRkOSrt27ZL054jSvn37HD4r5OHhoYoVK2rgwIGZWlz79u3t/37qqadUtWpVhYWFacWKFWrVqlW62wwdOtQe6qQ/pzMPDQ3N1LoAAAAA5G4ZDkqps9117dpVkyZNcsn3JQUHByssLExHjhy5Yx9PT887jjYBAAAAQEY4PZnD7TPQZbezZ8/qxIkTCg4OdlkNAAAAAHI/p4OSJG3btk3/+te/FB8frxs3bjisW7x4cYb3c/nyZf3888/25bi4OO3evVsBAQEKCAjQiBEj1Lp1awUHB+v48eP629/+poIFC+qll166n7IBAAAAIEOcnvVu/vz5qlWrlg4cOKAlS5bo5s2bOnDggNauXSt/f3+n9rV9+3ZVrlxZlStXlvTnhBGVK1fWO++8Izc3N+3bt08tW7ZU6dKl1aVLF5UuXVqbN2+Wn5+fs2UDAAAAQIY5PaI0duxYTZw4UW+88Yb8/Pw0adIkhYeHq2fPnk7fElevXj0ZY+64ftWqVc6WBwAAAAAPzOkRpaNHj6p58+aS/pw44cqVK7LZbOrfv78+//zzTC8QAAAAALKb00EpICBAly5dkiQVKVJE//vf/yRJFy5c0NWrVzO3OgAAAABwAadvvatTp45iYmIUERGhdu3a6S9/+YvWrl2rmJgYNWzYMCtqBAAAAIBs5XRQ+uc//6nr169L+vPLXd3d3bVx40a1atVKw4YNy/QCAQAAACC7OR2UAgIC7P9+7LHHNHjwYA0ePDhTiwIAAAAAV3L6M0pubm46ffp0mvazZ8/Kzc0tU4oCAAAAAFdyOijdaTrvpKQkeXh4PHBBAAAAAOBqGb717h//+IckyWazafr06cqbN699XXJysjZs2KCyZctmfoUAAAAAkM0yHJQmTpwo6c8RpSlTpjjcZufh4aHixYtrypQpmV8hAAAAAGSzDAeluLg4SVL9+vW1ePFi5c+fP8uKAgAAAABXcnrWu3Xr1mVFHQAAAACQY2QoKA0YMEDvvvuufH19NWDAgLv2nTBhQqYUBgAAAACukqGgtGvXLt28edP+7zux2WyZUxUAAAAAuFCGgtLtt9tx6x0AAACA3M7p71G6ePGizp07l6b93LlzSkxMzJSiAAAAAMCVnA5KHTp00Pz589O0L1y4UB06dMiUogAAAADAlZwOSlu3blX9+vXTtNerV09bt27NlKIAAAAAwJWcDkpJSUm6detWmvabN2/q2rVrmVIUAAAAALiS00HpmWee0eeff56mfcqUKXr66aczpSgAAAAAcCWnv3B2zJgxatSokfbs2aOGDRtKktasWaNt27Zp9erVmV4gAAAAAGQ3p0eUatWqpc2bNys0NFQLFy7UN998o5IlS2rv3r2qU6dOVtQIAAAAANnK6RElSapUqZLmzp2b2bUAAAAAQI7g9IgSAAAAAOR2BCUAAAAAsCAoAQAAAIAFQQkAAAAALO47KP38889atWqV/UtmjTGZVhQAAAAAuJLTQens2bNq1KiRSpcurWbNmikhIUGS1KNHD7311luZXiAAAAAAZDeng1L//v2VJ08excfHy8fHx97evn17rVy5MlOLAwAAAABXcPp7lFavXq1Vq1apaNGiDu2lSpXSL7/8kmmFAQAAAICrOD2idOXKFYeRpFR//PGHPD09M6UoAAAAAHAlp4NS3bp1NWfOHPuyzWZTSkqKxo8fr/r162dqcQAAAADgCk7fejd+/HjVq1dP27dv140bNzR48GDt379f586d0w8//JAVNQIAAABAtnJ6ROnJJ5/U3r17Va1aNT333HO6cuWKWrVqpV27dqlEiRJZUSMAAAAAZCunR5QkKSgoSCNHjszsWgAAAAAgR8hQUNq7d2+Gd1ihQoX7LgYAAAAAcoIMBaVKlSrJZrPJGCObzWZvN8ZIkkNbcnJyJpcIAAAAANkrQ59RiouL07FjxxQXF6dFixYpPDxcn332mXbv3q3du3frs88+U4kSJbRo0aKsrhcAAAAAslyGRpTCwsLs/27btq3+8Y9/qFmzZva2ChUqKDQ0VMOGDdOLL76Y6UUCAAAAQHZyeta7ffv2KTw8PE17eHi4Dhw4kClFAQAAAIArOR2UypUrp9GjR+v69ev2tqSkJI0ePVrlypXL1OIAAAAAwBWcnh58ypQpev755xUaGqqKFStKkvbs2SObzably5dneoEAAAAAkN2cDkrVqlVTXFycvvrqKx06dEjGGLVv316dOnWSr69vVtQIAAAAANnqvr5w1sfHR//3f/+X2bUAAAAAQI7g9GeUAAAAACC3IygBAAAAgAVBCQAAAAAsCEoAAAAAYHFfQenChQuaPn26hg4dqnPnzkmSdu7cqd9++y1TiwMAAAAAV3B61ru9e/eqUaNG8vf31/Hjx/Xaa68pICBAS5Ys0S+//KI5c+ZkRZ0AAAAAkG2cHlEaMGCAoqOjdeTIEXl5ednbo6KitGHDhkwtDgAAAABcwemgtG3bNvXs2TNNe5EiRXTq1KlMKQoAAAAAXMnpoOTl5aXExMQ07YcPH1ahQoUypSgAAAAAcCWng1LLli01atQo3bx5U5Jks9kUHx+vIUOGqHXr1pleIAAAAABkN6eD0ocffqgzZ84oMDBQ165dU2RkpEqWLCk/Pz+NGTMmK2oEAAAAgGzl9Kx3+fLl08aNG7V27Vrt3LlTKSkpqlKliho1apQV9QEAAABAtnM6KKVq0KCBGjRokJm1AAAAAECOcF9Bac2aNVqzZo1Onz6tlJQUh3VffPFFphQGAAAAAK7idFAaOXKkRo0apapVqyo4OFg2my0r6gIAAAAAl3E6KE2ZMkWzZs3Sq6++mhX1AAAAAIDLOT3r3Y0bN1SzZs2sqAUAAAAAcgSng1KPHj309ddfZ0UtAAAAAJAjOH3r3fXr1/X555/ru+++U4UKFeTu7u6wfsKECZlWHAAAAAC4gtNBae/evapUqZIk6X//+5/DOiZ2AAAAAJAbOB2U1q1blxV1AAAAAECO4fRnlAAAAAAgt3N6ROnKlSt677337viFs8eOHcu04gAAAADAFZwOSj169FBsbKxeffVVvnAWAAAAQK7kdFD69ttvtWLFCtWqVSsr6gEAAAAAl3P6M0r58+dXQEBAVtQCAAAAADmC00Hp3Xff1TvvvKOrV69mRT0AAAAA4HJO33r30Ucf6ejRoypcuLCKFy+e5gtnd+7cmWnFAQAAAIArOB2UXnzxxSwoAwAAAAByDqeD0vDhw7OiDgAAAADIMe7rC2cvXLig6dOna+jQoTp37pykP2+5++233zK1OAAAAABwBadHlPbu3atGjRrJ399fx48f12uvvaaAgAAtWbJEv/zyi+bMmZMVdQIAAABAtnF6RGnAgAGKjo7WkSNH5OXlZW+PiorShg0bMrU4AAAAAHAFp4PStm3b1LNnzzTtRYoU0alTpzKlKAAAAABwJaeDkpeXlxITE9O0Hz58WIUKFcqUogAAAADAlZwOSi1bttSoUaN08+ZNSZLNZlN8fLyGDBmi1q1bZ3qBAAAAAJDdnA5KH374oc6cOaPAwEBdu3ZNkZGRKlmypPz8/DRmzBin9rVhwwY9//zzCgkJkc1m09KlSx3WG2M0YsQIhYSEyNvbW/Xq1dP+/fudLRkAAAAAnOL0rHf58uXTxo0btXbtWu3cuVMpKSmqUqWKGjVq5PTBr1y5oooVK6pr167pjkZ98MEHmjBhgmbNmqXSpUtr9OjReu6553T48GH5+fk5fTwAAAAAyAing1JcXJzCw8PVoEEDNWjQ4IEOHhUVpaioqHTXGWP08ccf6+2331arVq0kSbNnz1bhwoX19ddfpzuhBAAAAABkBqdvvStZsqTq16+vr776StevX8+KmiT9GchOnTqlxo0b29s8PT0VGRmpTZs23XG7pKQkJSYmOjwAAAAAwBlOB6U9e/aocuXKeuuttxQUFKSePXtq69atmV5Y6lTjhQsXdmgvXLjwXachHzdunPz9/e2P0NDQTK8NAAAAQO7mdFB66qmnNGHCBP3222+aOXOmTp06pTp16qh8+fKaMGGCzpw5k6kF2mw2h2VjTJq22w0dOlQXL160P06cOJGp9QAAAADI/ZwOSqny5Mmjl156SQsXLtT777+vo0ePauDAgSpatKg6d+6shISEByosKChIktKMHp0+fTrNKNPtPD09lS9fPocHAAAAADjjvoPS9u3b1bt3bwUHB2vChAkaOHCgjh49qrVr1+q3335Ty5YtH6iw8PBwBQUFKSYmxt5248YNxcbGqmbNmg+0bwAAAAC4G6dnvZswYYJmzpypw4cPq1mzZpozZ46aNWumxx77M3OFh4dr6tSpKlu27D33dfnyZf3888/25bi4OO3evVsBAQEqVqyY3nzzTY0dO1alSpVSqVKlNHbsWPn4+KhTp07Olg0AAAAAGeZ0UJo8ebK6deumrl272m+PsypWrJhmzJhxz31t375d9evXty8PGDBAktSlSxfNmjVLgwcP1rVr19S7d2+dP39e1atX1+rVq/kOJQAAAABZymaMMa4uIislJibK399fFy9ezPLPKz09aE6W7h/IrXaM7+zqEjJN/KgIV5cAPJSKvbPP1SUAeAQ4kw2cHlGSpAsXLmjGjBk6ePCgbDabypUrp+7du8vf3/++CgYAAACAnMTpyRy2b9+uEiVKaOLEiTp37pz++OMPTZw4USVKlNDOnTuzokYAAAAAyFZOjyj1799fL7zwgqZNm6Y8ef7c/NatW+rRo4fefPNNbdiwIdOLBAAAAIDs5HRQ2r59u0NIkv78TqXBgweratWqmVocAAAAALiC07fe5cuXT/Hx8WnaT5w4wWx0AAAAAHIFp4NS+/bt1b17dy1YsEAnTpzQr7/+qvnz56tHjx7q2LFjVtQIAAAAANnK6VvvPvzwQ9lsNnXu3Fm3bt2SJLm7u6tXr1567733Mr1AAAAAAMhuTgclDw8PTZo0SePGjdPRo0dljFHJkiXl4+OTFfUBAAAAQLa7r+9RkiQfHx9FRPDFigAAAABynwwHpW7dumWo3xdffHHfxQAAAABATpDhoDRr1iyFhYWpcuXKMsZkZU0AAAAA4FIZDkqvv/665s+fr2PHjqlbt2565ZVXFBAQkJW1AQAAAIBLZHh68M8++0wJCQn661//qm+++UahoaFq166dVq1axQgTAAAAgFzFqe9R8vT0VMeOHRUTE6MDBw6ofPny6t27t8LCwnT58uWsqhEAAAAAspXTXzibymazyWazyRijlJSUzKwJAAAAAFzKqaCUlJSkefPm6bnnnlOZMmW0b98+/fOf/1R8fLzy5s2bVTUCAAAAQLbK8GQOvXv31vz581WsWDF17dpV8+fPV4ECBbKyNgAAAABwiQwHpSlTpqhYsWIKDw9XbGysYmNj0+23ePHiTCsOAAAAAFwhw0Gpc+fOstlsWVkLAAAAAOQITn3hLAAAAAA8Cu571jsAAAAAyK0ISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWGZ4eHAAAABlT65Nari4BeOj80PcHV5fggBElAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAIscHZRGjBghm83m8AgKCnJ1WQAAAAByuTyuLuBeypcvr++++86+7Obm5sJqAAAAADwKcnxQypMnD6NIAAAAALJVjr71TpKOHDmikJAQhYeHq0OHDjp27Nhd+yclJSkxMdHhAQAAAADOyNFBqXr16pozZ45WrVqladOm6dSpU6pZs6bOnj17x23GjRsnf39/+yM0NDQbKwYAAACQG+TooBQVFaXWrVsrIiJCjRo10ooVKyRJs2fPvuM2Q4cO1cWLF+2PEydOZFe5AAAAAHKJHP8Zpdv5+voqIiJCR44cuWMfT09PeXp6ZmNVAAAAAHKbHD2iZJWUlKSDBw8qODjY1aUAAAAAyMVydFAaOHCgYmNjFRcXp61bt6pNmzZKTExUly5dXF0aAAAAgFwsR9969+uvv6pjx476448/VKhQIT377LPasmWLwsLCXF0aAAAAgFwsRwel+fPnu7oEAAAAAI+gHH3rHQAAAAC4AkEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsCAoAQAAAIAFQQkAAAAALAhKAAAAAGBBUAIAAAAAC4ISAAAAAFgQlAAAAADAgqAEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACABUEJAAAAACwISgAAAABgQVACAAAAAAuCEgAAAABYEJQAAAAAwIKgBAAAAAAWBCUAAAAAsHgogtJnn32m8PBweXl56emnn9b333/v6pIAAAAA5GI5PigtWLBAb775pt5++23t2rVLderUUVRUlOLj411dGgAAAIBcKscHpQkTJqh79+7q0aOHypUrp48//lihoaGaPHmyq0sDAAAAkEvlcXUBd3Pjxg3t2LFDQ4YMcWhv3LixNm3alO42SUlJSkpKsi9fvHhRkpSYmJh1hf5/yUnXsvwYQG6UHddndrl0PdnVJQAPpdz0PiBJt67dcnUJwEMnO94HUo9hjLln3xwdlP744w8lJyercOHCDu2FCxfWqVOn0t1m3LhxGjlyZJr20NDQLKkRwIPz/+R1V5cAwNXG+bu6AgAu5v/X7HsfuHTpkvz97368HB2UUtlsNodlY0yatlRDhw7VgAED7MspKSk6d+6cChQocMdtkLslJiYqNDRUJ06cUL58+VxdDgAX4b0AAO8DMMbo0qVLCgkJuWffHB2UChYsKDc3tzSjR6dPn04zypTK09NTnp6eDm2PP/54VpWIh0i+fPl4UwTAewEA3gcecfcaSUqVoydz8PDw0NNPP62YmBiH9piYGNWsWdNFVQEAAADI7XL0iJIkDRgwQK+++qqqVq2qGjVq6PPPP1d8fLxef53PNAAAAADIGjk+KLVv315nz57VqFGjlJCQoKeeekr//e9/FRYW5urS8JDw9PTU8OHD09ySCeDRwnsBAN4H4AybycjceAAAAADwCMnRn1ECAAAAAFcgKAEAAACABUEJAAAAACwISrgnm82mpUuX2pcPHTqkZ599Vl5eXqpUqZKOHz8um82m3bt3Z2h/0dHRevHFFzN8fGf3n5Wstaxfv142m00XLlyQJM2aNYvv7QLuYMSIEapUqdJd+9SrV09vvvlmttSTE/EeAgA5B0Epl9q0aZPc3NzUtGnTDG9zp19iEhISFBUVZV8ePny4fH19dfjwYa1Zs0ahoaH2GQkzYtKkSZo1a1aG68oIZ3+5yqrw1b59e/3000+Zuk8gq0RHR8tms6X7dQu9e/eWzWZTdHR0tta0ePFivfvuu1l6jJz0xxcr3kMA56W+l7333nsO7UuXLpXNZpOU9g+bViNGjJDNZpPNZpObm5tCQ0PVo0cPnTlzJqvLRw5GUMqlvvjiC/Xt21cbN25UfHz8XfsaY3Tr1q07rg8KCnKYRvPo0aOqXbu2wsLCVKBAAbm5uSkoKEh58mRstnl/f/9c+xdTb29vBQYGuroMIMNCQ0M1f/58Xbt2zd52/fp1zZs3T8WKFcv2egICAuTn55ftx81qN27cyFA/3kOA++Pl5aX3339f58+fv+99lC9fXgkJCYqPj9fkyZP1zTffqHPnzplYJR42BKVc6MqVK1q4cKF69eqlFi1apBm9Sf2ryqpVq1S1alV5enrqyy+/1MiRI7Vnzx77X1RSt7v91jubzaYdO3Zo1KhRstlsGjFiRLp/nd2/f7+aN2+ufPnyyc/PT3Xq1NHRo0clpb31buXKlapdu7Yef/xxFShQQC1atLD3vV/FixfX2LFj1a1bN/n5+alYsWL6/PPP7evDw8MlSZUrV5bNZlO9evXs62bOnKly5crJy8tLZcuW1WeffZbh46Z328zo0aMVGBgoPz8/9ejRQ0OGDEkzcne3Y6ae38WLF6t+/fry8fFRxYoVtXnzZod9/PDDD4qMjJSPj4/y58+vJk2a2P/DMMbogw8+0BNPPCFvb29VrFhR//73vzP8vJB7ValSRcWKFdPixYvtbYsXL1ZoaKgqV67s0Dcj1+qvv/6qDh06KCAgQL6+vqpataq2bt3q0OfLL79U8eLF5e/vrw4dOujSpUv2ddbR4Xtdy5L022+/qX379sqfP78KFCigli1b6vjx4/d9Tu51vSQnJ6t79+4KDw+Xt7e3ypQpo0mTJjnsI/V9bty4cQoJCVHp0qUzdC1b30NSR/rvds4uXbqkl19+Wb6+vgoODtbEiRMf+VsY8ehp1KiRgoKCNG7cuPveR548eRQUFKQiRYqoRYsW6tevn1avXu3whyQ8WghKudCCBQtUpkwZlSlTRq+88opmzpyp9L4ua/DgwRo3bpwOHjyoxo0b66233rL/NSUhIUHt27dPs01CQoLKly+vt956SwkJCRo4cGCaPr/99pvq1q0rLy8vrV27Vjt27FC3bt3uOGp15coVDRgwQNu2bdOaNWv02GOP6aWXXlJKSsoDnYePPvpIVatW1a5du9S7d2/16tVLhw4dkiT9+OOPkqTvvvtOCQkJ9l8Sp02bprfffltjxozRwYMHNXbsWA0bNkyzZ8++rxrmzp2rMWPG6P3339eOHTtUrFgxTZ482aFPRo/59ttva+DAgdq9e7dKly6tjh072s/p7t271bBhQ5UvX16bN2/Wxo0b9fzzzys5OVmS9Pe//10zZ87U5MmTtX//fvXv31+vvPKKYmNj7+t5IXfp2rWrZs6caV/+4osv1K1btzT97nWtXr58WZGRkTp58qSWLVumPXv2aPDgwQ7X8tGjR7V06VItX75cy5cvV2xsbJrbZazudi1fvXpV9evXV968ebVhwwZt3LhRefPmVdOmTTM8imN1r+slJSVFRYsW1cKFC3XgwAG98847+tvf/qaFCxc67GfNmjU6ePCgYmJitHz5cnv73a7l9NzrnA0YMEA//PCDli1bppiYGH3//ffauXPnfT134GHl5uamsWPH6pNPPtGvv/6aKfv09vZWSkrKXa9P5HIGuU7NmjXNxx9/bIwx5ubNm6ZgwYImJibGvn7dunVGklm6dKnDdsOHDzcVK1ZMsz9JZsmSJfblihUrmuHDh9uX4+LijCSza9cuY4wxQ4cONeHh4ebGjRvp1telSxfTsmXLO9Z/+vRpI8ns27cv3f2nJzIy0vzlL3+xL4eFhZlXXnnFvpySkmICAwPN5MmT77rP0NBQ8/XXXzu0vfvuu6ZGjRrpbpd6Ls+fP2+MMWbmzJnG39/fvm316tXNG2+84bC/WrVqOZznjB5z+vTp9vX79+83kszBgweNMcZ07NjR1KpVK91zc/nyZePl5WU2bdrk0N69e3fTsWPHdLfBoyH1Wjxz5ozx9PQ0cXFx5vjx48bLy8ucOXPGtGzZ0nTp0uWO21uv1alTpxo/Pz9z9uzZdPsPHz7c+Pj4mMTERHvboEGDTPXq1e3Lzl7LM2bMMGXKlDEpKSn2PklJScbb29usWrUq3Tru9p5yv9dL7969TevWre3LXbp0MYULFzZJSUlpjnu3a9n6HnKvc5aYmGjc3d3Nv/71L/v6CxcuGB8fH4fzCORmt/9e8eyzz5pu3boZY4xZsmSJSf1V1/r/tZX1d6CDBw+akiVLmmrVqmVl6cjhMvahEjw0Dh8+rB9//NE+QpInTx61b99eX3zxhRo1auTQt2rVqllSw+7du1WnTh25u7tnqP/Ro0c1bNgwbdmyRX/88Yf9r8/x8fEZniAiPRUqVLD/22azKSgoSKdPn75j/zNnzujEiRPq3r27XnvtNXv7rVu35O/vf181HD58WL1793Zoq1atmtauXev0MW9/PsHBwZKk06dPq2zZstq9e7fatm2bbg0HDhzQ9evX9dxzzzm037hxI82tVXg0FSxYUM2bN9fs2bNljFHz5s1VsGDBNP3uda3u3r1blStXVkBAwB2PVbx4cYfPIAUHB9/1upTufi3v2LFDP//8c5rPNV2/fv2+buHN6PUyZcoUTZ8+Xb/88ouuXbumGzdupLmlNiIiQh4eHnd9PtZrOT13O2fHjh3TzZs3Va1aNft6f39/lSlTJoPPGMhd3n//fTVo0EBvvfWW09vu27dPefPmVXJyspKSklSvXr00t/ri0UJQymVmzJihW7duqUiRIvY2Y4zc3d11/vx55c+f397u6+ubJTV4e3s71f/5559XaGiopk2bppCQEKWkpOipp56679tmUlmDms1mu+vtfKnrpk2bpurVqzusc3Nzu+86UmfcSWVuuw3SmWPe/nxS95m6/d3OeWqfFStWOLwuJDlM0oFHW7du3dSnTx9J0qeffppun3tdqxm59p29Lu+1TUpKip5++mnNnTs3zXaFChW6Zz1WGbleFi5cqP79++ujjz5SjRo15Ofnp/Hjx6f5LNad3mPvdi3fq3/qNqn9U99P7vY+AzxK6tatqyZNmuhvf/ub07N2lilTRsuWLZObm5tCQkL4PxIEpdzk1q1bmjNnjj766CM1btzYYV3r1q01d+5c+y9C6fHw8LB/puVBVKhQQbNnz9bNmzfvOap09uxZHTx4UFOnTlWdOnUkSRs3bnzgGu4l9a+8tz/fwoULq0iRIjp27JhefvnlTDlOmTJl9OOPP+rVV1+1t23fvj3Tj1mhQgWtWbNGI0eOTLPuySeflKenp+Lj4xUZGXnfx0Dudvtnepo0aZJmfUau1QoVKmj69Ok6d+7cXUeVMlOVKlW0YMECBQYGKl++fA+8v4xcL99//71q1qzpMFr8oBPQ3K8SJUrI3d1dP/74o0JDQyVJiYmJOnLkCNc7HlnvvfeeKlWqpNKlSzu1nYeHh0qWLJlFVeFhRFDKRZYvX67z58+re/fuaW7batOmjWbMmHHXoFS8eHHFxcVp9+7dKlq0qPz8/O7rryl9+vTRJ598og4dOmjo0KHy9/fXli1bVK1atTS3g6TOUvX5558rODhY8fHxGjJkiNPHdFZgYKC8vb21cuVKFS1aVF5eXvL399eIESPUr18/5cuXT1FRUUpKStL27dt1/vx5DRgwwOnj9O3bV6+99pqqVq2qmjVrasGCBdq7d6+eeOIJe5/MOObQoUMVERGh3r176/XXX5eHh4fWrVuntm3bqmDBgho4cKD69++vlJQU1a5dW4mJidq0aZPy5s2rLl26OP28kPu4ubnp4MGD9n9bZeRa7dixo8aOHWuf7S04OFi7du1SSEiIatSokSV1v/zyyxo/frxatmypUaNGqWjRooqPj9fixYs1aNAgFS1a9I7bHj58OE3bk08+ec/rpWTJkpozZ45WrVql8PBwffnll9q2bZt9Ns3s5Ofnpy5dumjQoEEKCAhQYGCghg8frsceeyzNKBPwqIiIiNDLL7+sTz75JM26ffv2pblV915fhI1HF7Pe5SIzZsxQo0aN0v08TevWrbV79+67zoTUunVrNW3aVPXr11ehQoU0b968+6qjQIECWrt2rX0GrKefflrTpk1Ld3Tpscce0/z587Vjxw499dRT6t+/v8aPH39fx3VGnjx59I9//ENTp05VSEiIWrZsKUnq0aOHpk+frlmzZikiIkKRkZGaNWvWff8C9PLLL2vo0KEaOHCgqlSpori4OEVHR8vLy8veJzOOWbp0aa1evVp79uxRtWrVVKNGDf3nP/+xf7fVu+++q3feeUfjxo1TuXLl1KRJE33zzTcu+cUOOVe+fPnuOCqTkWvVw8NDq1evVmBgoJo1a6aIiAi99957D3Tr6r34+Phow4YNKlasmFq1aqVy5cqpW7duunbt2j1HmDp06KDKlSs7PE6ePHnP6+X1119Xq1at1L59e1WvXl1nz55N81nE7DRhwgTVqFFDLVq0UKNGjVSrVi371w0Aj6p333033VtQ69atm+a6B+7EZriRGchWzz33nIKCgvTll1+6uhQAudCVK1dUpEgRffTRR+revburywGAhxa33gFZ6OrVq5oyZYqaNGkiNzc3zZs3T999951iYmJcXRqAXGLXrl06dOiQqlWrposXL2rUqFGSZB8pBwDcH4ISkIVsNpv++9//avTo0UpKSlKZMmW0aNGiNFO1A8CD+PDDD3X48GF5eHjo6aef1vfff5/uFO8AgIzj1jsAAAAAsGAyBwAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgDgPq1fv16TJ092dRkAgCxAUAIA4A7Wr18vm82mCxcupFkXFxenV155Rc8880z2FwYAyHIEJQBAjhAdHS2bzabXX389zbrevXvLZrMpOjo6Q/u6W8DJDDdu3FDHjh01bdo0Va1aNUuOAQBwLYISACDHCA0N1fz583Xt2jV72/Xr1zVv3jwVK1bMhZU58vDw0JYtWxQVFeXqUgAAWYSgBADIMapUqaJixYpp8eLF9rbFixcrNDRUlStXtrclJSWpX79+CgwMlJeXl2rXrq1t27ZJko4fP6769etLkvLnz3/PkaikpCQNHjxYoaGh8vT0VKlSpTRjxow79t+0aZPq1q0rb29vhYaGql+/frpy5coDPnMAQE5DUAIA5Chdu3bVzJkz7ctffPGFunXr5tBn8ODBWrRokWbPnq2dO3eqZMmSatKkic6dO6fQ0FAtWrRIknT48GElJCRo0qRJdzxe586dNX/+fP3jH//QwYMHNWXKFOXNmzfdvvv27VOTJk3UqlUr7d27VwsWLNDGjRvVp0+fTHjmAICcxGaMMa4uAgCA6OhoXbhwQdOnT1fRokV16NAh2Ww2lS1bVidOnFCPHj30+OOP69NPP1X+/Pk1a9YsderUSZJ08+ZNFS9eXG+++aYGDRqk9evXq379+jp//rwef/zxOx7zp59+UpkyZRQTE6NGjRqlWW/dT+fOneXt7a2pU6fa+2zcuFGRkZG6cuWKvLy8Mv28AABcI4+rCwAA4HYFCxZU8+bNNXv2bBlj1Lx5cxUsWNC+/ujRo7p586Zq1aplb3N3d1e1atV08ODBO+537ty56tmzp33522+/VUJCgtzc3BQZGZmh2nbs2KGff/5Zc+fOtbcZY5SSkqK4uDiVK1fOmacKAMjBCEoAgBynW7du9tvZPv30U4d1qTdC2Gy2NO3Wttu98MILql69un25SJEi+u6775yqKyUlRT179lS/fv3SrMtJk00AAB4cQQkAkOM0bdpUN27ckCQ1adLEYV3JkiXl4eGhjRs3Otx6t337dr355puS/pyVTpKSk5Pt2/n5+cnPz89hXxEREUpJSVFsbGy6t95ZValSRfv371fJkiXv+7kBAB4OTOYAAMhx3NzcdPDgQR08eFBubm4O63x9fdWrVy8NGjRIK1eu1IEDB/Taa6/p6tWr6t69uyQpLCxMNptNy5cv15kzZ3T58uV0j1O8eHF16dJF3bp109KlSxUXF6f169dr4cKF6fb/61//qs2bN+uNN97Q7t27deTIES1btkx9+/bN3BMAAHA5ghIAIEfKly+f8uXLl+669957T61bt9arr76qKlWq6Oeff9aqVauUP39+SX/eVjdy5EgNGTJEhQsXvuusdJMnT1abNm3Uu3dvlS1bVq+99todp/uuUKGCYmNjdeTIEdWpU0eVK1fWsGHDFBwc/OBPGACQozDrHQAAAABYMKIEAAAAABYEJQAAAACwICgBAAAAgAVBCQAAAAAsCEoAAAAAYEFQAgAAAAALghIAAAAAWBCUAAAAAMCCoAQAAAAAFgQlAAAAALAgKAEAAACAxf8D12o/HBUWk2sAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# analyse avancée sur les publications\n",
        "\n",
        "# analyse de moyen de citation par keyword\n",
        "df_keyword = df.groupby('Keyword')['Citation Count'].mean().reset_index()\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x='Keyword', y='Citation Count', data=df_keyword)\n",
        "plt.title('Moyenne de citations par mot-clé')\n",
        "plt.xlabel('Mot-clé')\n",
        "plt.ylabel('Moyenne de citations')\n",
        "plt.show()\n",
        "\n",
        "# les auteurs les plus cités\n",
        "authors_series = df['Authors'].str.split(',').explode().value_counts()\n",
        "top_authors = authors_series.head(10)\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x=top_authors.values, y=top_authors.index)\n",
        "plt.title('Top 10 des auteurs les plus cités')\n",
        "plt.xlabel('Nombre de citations')\n",
        "plt.ylabel('Auteurs')\n",
        "plt.show()\n",
        "\n",
        "# les auteurs les plus cités par keyword\n",
        "df_keyword = df.groupby('Keyword')['Authors'].apply(lambda x: x.str.split(',').explode().value_counts().head(1)).reset_index()\n",
        "df_keyword.columns = ['Keyword', 'Top Author', 'Citation Count']\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x='Citation Count', y='Top Author', hue='Keyword', data=df_keyword)\n",
        "plt.title('Top auteur par mot-clé')\n",
        "plt.xlabel('Nombre de citations')\n",
        "plt.ylabel('Auteur')\n",
        "plt.show()\n",
        "\n",
        "# les sujets les plus cités\n",
        "publication_names_series = df['Publication Name'].value_counts()\n",
        "top_publication_names = publication_names_series.head(10)\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x=top_publication_names.values, y=top_publication_names.index)\n",
        "plt.title('Top 10 des sujets les plus cités')\n",
        "plt.xlabel('Nombre de citations')\n",
        "plt.ylabel('Sujets')\n",
        "plt.show()\n",
        "\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.11.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}