ARXIV_Time_Series_Final.ipynb

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        "<a href=\"https://colab.research.google.com/github/MatthewK84/LinkedIn-Learning-Journey/blob/main/ARXIV_Time_Series_Final.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bfacb0ba",
      "metadata": {
        "id": "bfacb0ba"
      },
      "outputs": [],
      "source": [
        "!pip install arxiv --user"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5ad512df",
      "metadata": {
        "id": "5ad512df"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import arxiv"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "81141bbc",
      "metadata": {
        "id": "81141bbc"
      },
      "outputs": [],
      "source": [
        "# i only use these if I want to remove annoying deprecation warnings from my analysis\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4d25a4b2",
      "metadata": {
        "id": "4d25a4b2"
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      "source": [
        "def search_arxiv(query, max_results=10):\n",
        "\n",
        "    data = {}\n",
        "    i = 0\n",
        "\n",
        "    search = arxiv.Search(query=query, max_results=max_results)\n",
        "\n",
        "    for result in search.results():\n",
        "\n",
        "        try:\n",
        "\n",
        "            data[i] = {}\n",
        "\n",
        "            data[i]['title'] = result.title\n",
        "            data[i]['date_published'] = result.published\n",
        "            data[i]['authors'] = [a.name for a in result.authors]\n",
        "            data[i]['summary'] = result.summary\n",
        "            data[i]['url'] = result.pdf_url\n",
        "            data[i]['category'] = result.primary_category\n",
        "\n",
        "        except:\n",
        "\n",
        "            print('weird arxiv error')\n",
        "\n",
        "        # there are more fields that can be added; add as many as you need\n",
        "\n",
        "        i += 1\n",
        "\n",
        "    df = pd.DataFrame(data).T\n",
        "    df = df[['date_published', 'title', 'authors', 'summary', 'url', 'category']]\n",
        "    df['date_published'] = pd.to_datetime(df['date_published'])\n",
        "    df.sort_values('date_published', ascending=False, inplace=True)\n",
        "    df.reset_index(drop=True, inplace=True)\n",
        "    return df"
      ]
    },
    {
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            "text/plain": [
              "             date_published  \\\n",
              "0 2023-11-06 17:52:08+00:00   \n",
              "1 2023-11-06 15:32:50+00:00   \n",
              "2 2023-11-06 05:01:58+00:00   \n",
              "3 2023-11-05 04:27:24+00:00   \n",
              "4 2023-11-05 04:21:42+00:00   \n",
              "\n",
              "                                               title  \\\n",
              "0  TS-Diffusion: Generating Highly Complex Time S...   \n",
              "1  Few-shot Learning using Data Augmentation and ...   \n",
              "2  Dynamic Neural Fields for Learning Atlases of ...   \n",
              "3  Time Series Synthesis Using the Matrix Profile...   \n",
              "4  Ego-Network Transformer for Subsequence Classi...   \n",
              "\n",
              "                                             authors  \\\n",
              "0                                      [Yangming Li]   \n",
              "1  [Hao Zhang, Zhendong Pang, Jiangpeng Wang, Ten...   \n",
              "2  [Zeen Chi, Zhongxiao Cong, Clinton J. Wang, Yi...   \n",
              "3  [Audrey Der, Chin-Chia Michael Yeh, Yan Zheng,...   \n",
              "4  [Chin-Chia Michael Yeh, Huiyuan Chen, Yujie Fa...   \n",
              "\n",
              "                                             summary  \\\n",
              "0  While current generative models have achieved ...   \n",
              "1  Deep neural networks (DNNs) that tackle the ti...   \n",
              "2  We present a method for fast biomedical image ...   \n",
              "3  Publishing and sharing data is crucial for the...   \n",
              "4  Time series classification is a widely studied...   \n",
              "\n",
              "                                 url category  \n",
              "0  http://arxiv.org/pdf/2311.03303v1    cs.LG  \n",
              "1  http://arxiv.org/pdf/2311.03194v1    cs.CV  \n",
              "2  http://arxiv.org/pdf/2311.02874v1  eess.IV  \n",
              "3  http://arxiv.org/pdf/2311.02563v1    cs.DB  \n",
              "4  http://arxiv.org/pdf/2311.02561v1    cs.LG  "
            ],
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              "      <td>2023-11-06 17:52:08+00:00</td>\n",
              "      <td>TS-Diffusion: Generating Highly Complex Time S...</td>\n",
              "      <td>[Yangming Li]</td>\n",
              "      <td>While current generative models have achieved ...</td>\n",
              "      <td>http://arxiv.org/pdf/2311.03303v1</td>\n",
              "      <td>cs.LG</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2023-11-06 15:32:50+00:00</td>\n",
              "      <td>Few-shot Learning using Data Augmentation and ...</td>\n",
              "      <td>[Hao Zhang, Zhendong Pang, Jiangpeng Wang, Ten...</td>\n",
              "      <td>Deep neural networks (DNNs) that tackle the ti...</td>\n",
              "      <td>http://arxiv.org/pdf/2311.03194v1</td>\n",
              "      <td>cs.CV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2023-11-06 05:01:58+00:00</td>\n",
              "      <td>Dynamic Neural Fields for Learning Atlases of ...</td>\n",
              "      <td>[Zeen Chi, Zhongxiao Cong, Clinton J. Wang, Yi...</td>\n",
              "      <td>We present a method for fast biomedical image ...</td>\n",
              "      <td>http://arxiv.org/pdf/2311.02874v1</td>\n",
              "      <td>eess.IV</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2023-11-05 04:27:24+00:00</td>\n",
              "      <td>Time Series Synthesis Using the Matrix Profile...</td>\n",
              "      <td>[Audrey Der, Chin-Chia Michael Yeh, Yan Zheng,...</td>\n",
              "      <td>Publishing and sharing data is crucial for the...</td>\n",
              "      <td>http://arxiv.org/pdf/2311.02563v1</td>\n",
              "      <td>cs.DB</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2023-11-05 04:21:42+00:00</td>\n",
              "      <td>Ego-Network Transformer for Subsequence Classi...</td>\n",
              "      <td>[Chin-Chia Michael Yeh, Huiyuan Chen, Yujie Fa...</td>\n",
              "      <td>Time series classification is a widely studied...</td>\n",
              "      <td>http://arxiv.org/pdf/2311.02561v1</td>\n",
              "      <td>cs.LG</td>\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# at 1000 articles, it takes about 30 seconds\n",
        "# at 10000 articles, it takes a while; put laptop down and walk away\n",
        "# i haven't tried beyond 10000 yet\n",
        "\n",
        "# at the moment, this is finicky beyond 10000; needs debugged or something\n",
        "# could also be a problem with the arxiv library as it seems to be inside the search\n",
        "\n",
        "# so far, i haven't noticed obvious throttling, but possibly throttling is causing the issue\n",
        "\n",
        "query = 'Time Series'\n",
        "\n",
        "max_results = 5100\n",
        "\n",
        "df = search_arxiv(query, max_results)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "26b9e534",
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "26b9e534",
        "outputId": "2417e870-598c-4ae7-c82d-8fc8037eeea6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5100, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "46b3d673",
      "metadata": {
        "id": "46b3d673"
      },
      "outputs": [],
      "source": [
        "outfile = '/content/sample_data/arxix_time_series_data.csv'\n",
        "\n",
        "df.to_csv(outfile, index=False)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Time_Series = pd.read_csv('/content/sample_data/arxix_time_series_data.csv')\n",
        "Time_Series.head()"
      ],
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          "data": {
            "text/plain": [
              "              date_published  \\\n",
              "0  2023-11-06 17:52:08+00:00   \n",
              "1  2023-11-06 15:32:50+00:00   \n",
              "2  2023-11-06 05:01:58+00:00   \n",
              "3  2023-11-05 04:27:24+00:00   \n",
              "4  2023-11-05 04:21:42+00:00   \n",
              "\n",
              "                                               title  \\\n",
              "0  TS-Diffusion: Generating Highly Complex Time S...   \n",
              "1  Few-shot Learning using Data Augmentation and ...   \n",
              "2  Dynamic Neural Fields for Learning Atlases of ...   \n",
              "3  Time Series Synthesis Using the Matrix Profile...   \n",
              "4  Ego-Network Transformer for Subsequence Classi...   \n",
              "\n",
              "                                             authors  \\\n",
              "0                                    ['Yangming Li']   \n",
              "1  ['Hao Zhang', 'Zhendong Pang', 'Jiangpeng Wang...   \n",
              "2  ['Zeen Chi', 'Zhongxiao Cong', 'Clinton J. Wan...   \n",
              "3  ['Audrey Der', 'Chin-Chia Michael Yeh', 'Yan Z...   \n",
              "4  ['Chin-Chia Michael Yeh', 'Huiyuan Chen', 'Yuj...   \n",
              "\n",
              "                                             summary  \\\n",
              "0  While current generative models have achieved ...   \n",
              "1  Deep neural networks (DNNs) that tackle the ti...   \n",
              "2  We present a method for fast biomedical image ...   \n",
              "3  Publishing and sharing data is crucial for the...   \n",
              "4  Time series classification is a widely studied...   \n",
              "\n",
              "                                 url category  \n",
              "0  http://arxiv.org/pdf/2311.03303v1    cs.LG  \n",
              "1  http://arxiv.org/pdf/2311.03194v1    cs.CV  \n",
              "2  http://arxiv.org/pdf/2311.02874v1  eess.IV  \n",
              "3  http://arxiv.org/pdf/2311.02563v1    cs.DB  \n",
              "4  http://arxiv.org/pdf/2311.02561v1    cs.LG  "
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          "metadata": {},
          "execution_count": 7
        }
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    {
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      "source": [
        "Time_Series['category'].value_counts()"
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        "outputId": "24038ba0-3253-4a37-af23-f71ebdc64ef1",
        "colab": {
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      "id": "5Q7-7_TUWRdv",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cs.LG              1446\n",
              "stat.ME             482\n",
              "math.ST             264\n",
              "stat.ML             248\n",
              "physics.data-an     177\n",
              "                   ... \n",
              "cs.PF                 1\n",
              "math.SG               1\n",
              "cs.GL                 1\n",
              "math.GT               1\n",
              "q-bio.SC              1\n",
              "Name: category, Length: 140, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import csv\n",
        "\n",
        "# The path to the CSV file\n",
        "input_file_path = '/content/sample_data/arxix_time_series_data.csv'\n",
        "output_file_path = '/content/sample_data/time_series_filtered.csv'\n",
        "\n",
        "\n",
        "# List of categories to include\n",
        "included_categories = ['cs.LG', 'stat.ME', 'math.ST', 'stat.ML']\n",
        "\n",
        "# Read the CSV file and filter rows\n",
        "with open(input_file_path, mode='r', newline='', encoding='utf-8') as file:\n",
        "    reader = csv.DictReader(file)\n",
        "    fieldnames = reader.fieldnames  # Capture the fieldnames for writing\n",
        "\n",
        "    # Write the filtered rows to a new CSV file\n",
        "    with open(output_file_path, mode='w', newline='', encoding='utf-8') as outfile:\n",
        "        writer = csv.DictWriter(outfile, fieldnames=fieldnames)\n",
        "        writer.writeheader()  # Write the header to the output file\n",
        "\n",
        "        for row in reader:\n",
        "            # Check if the 'category' is in the list of included categories\n",
        "            if row['category'] in included_categories:\n",
        "                writer.writerow(row)"
      ],
      "metadata": {
        "id": "U2JaO5KWWa1w"
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      "id": "U2JaO5KWWa1w",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Time_Series_Final = pd.read_csv('/content/sample_data/time_series_filtered.csv')\n",
        "Time_Series_Final.head()"
      ],
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        "id": "qF0Vz1lRWzHv",
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      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "              date_published  \\\n",
              "0  2023-11-06 17:52:08+00:00   \n",
              "1  2023-11-05 04:21:42+00:00   \n",
              "2  2023-11-04 02:07:47+00:00   \n",
              "3  2023-11-02 17:58:09+00:00   \n",
              "4  2023-11-02 17:55:41+00:00   \n",
              "\n",
              "                                               title  \\\n",
              "0  TS-Diffusion: Generating Highly Complex Time S...   \n",
              "1  Ego-Network Transformer for Subsequence Classi...   \n",
              "2  Successive Model-Agnostic Meta-Learning for Fe...   \n",
              "3  Time Series Anomaly Detection using Diffusion-...   \n",
              "4  Deep Double Descent for Time Series Forecastin...   \n",
              "\n",
              "                                             authors  \\\n",
              "0                                    ['Yangming Li']   \n",
              "1  ['Chin-Chia Michael Yeh', 'Huiyuan Chen', 'Yuj...   \n",
              "2              ['Hai Su', 'Jiajun Hu', 'Songsen Yu']   \n",
              "3  ['Ioana Pintilie', 'Andrei Manolache', 'Florin...   \n",
              "4  ['Valentino Assandri', 'Sam Heshmati', 'Burhan...   \n",
              "\n",
              "                                             summary  \\\n",
              "0  While current generative models have achieved ...   \n",
              "1  Time series classification is a widely studied...   \n",
              "2  Meta learning is a promising technique for sol...   \n",
              "3  Diffusion models have been recently used for a...   \n",
              "4  Deep learning models, particularly Transformer...   \n",
              "\n",
              "                                 url category  \n",
              "0  http://arxiv.org/pdf/2311.03303v1    cs.LG  \n",
              "1  http://arxiv.org/pdf/2311.02561v1    cs.LG  \n",
              "2  http://arxiv.org/pdf/2311.02300v1    cs.LG  \n",
              "3  http://arxiv.org/pdf/2311.01452v1    cs.LG  \n",
              "4  http://arxiv.org/pdf/2311.01442v1    cs.LG  "
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              "      <td>['Chin-Chia Michael Yeh', 'Huiyuan Chen', 'Yuj...</td>\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-82c8552d-08fa-4117-9e1e-066b4f39845b 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-82c8552d-08fa-4117-9e1e-066b4f39845b');\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-4ba767ba-e803-442e-b2c5-ef6a15ea173f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4ba767ba-e803-442e-b2c5-ef6a15ea173f')\"\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-4ba767ba-e803-442e-b2c5-ef6a15ea173f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Time_Series_Final.shape"
      ],
      "metadata": {
        "id": "rjrYxGp9XB9I",
        "outputId": "57143a56-1721-497a-8aed-3c56739ac21e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "rjrYxGp9XB9I",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2440, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import csv\n",
        "from collections import Counter\n",
        "from wordcloud import WordCloud\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# The path to the CSV file\n",
        "input_file_path = '/content/sample_data/time_series_filtered.csv'\n",
        "\n",
        "# Common English stop words\n",
        "stop_words = set(['as','is','a','are','it','paper','based','proposed', 'an','approach', 'using', 'time-series', 'learning', 'performance', 'results', 'these', 'show', 'have', 'has','datasets','propose','classification','two','also','used','different','analysis','problem','new','framework', 'novel','been','demonstrate',\n",
        "                  'that', 'this', 'be', 'which', 'proposed', 'time', 'data', 'method', 'methods', 'our', 'we', 'series', 'forecasting', 'model', 'models', 'the', 'model', 'and', 'or', 'but', 'if', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'many', 'however', 'existing', 'its', 'prediction', 'application',\n",
        "                  'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', 'should', 'now'])\n",
        "\n",
        "# Initialize a Counter for word frequencies\n",
        "word_freq = Counter()\n",
        "\n",
        "# Read the CSV file and process the 'summary' column\n",
        "with open(input_file_path, mode='r', newline='', encoding='utf-8') as file:\n",
        "    reader = csv.DictReader(file)\n",
        "\n",
        "    for row in reader:\n",
        "        summary = row['summary']\n",
        "        # Tokenize the summary text\n",
        "        words = summary.split()\n",
        "        # Filter out stop words and count word frequencies\n",
        "        for word in words:\n",
        "            word = word.lower().rstrip('.!,')\n",
        "            if word not in stop_words:\n",
        "                word_freq[word] += 1\n",
        "\n",
        "# Select the ten most common words\n",
        "most_common_words = word_freq.most_common(10)\n",
        "\n",
        "# Generate the word cloud\n",
        "wordcloud = WordCloud(width=800, height=400, background_color ='white').generate_from_frequencies(dict(most_common_words))\n",
        "\n",
        "# Display the word cloud using matplotlib\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.imshow(wordcloud, interpolation='bilinear')\n",
        "plt.axis('off')  # Remove the axis\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "DJ63GMA7j-hK",
        "outputId": "2ff6b0d6-f5c0-485d-cd7e-41f2995fcf13"
      },
      "id": "DJ63GMA7j-hK",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Sample lists of Foundation and Advanced Methods\n",
        "Foundation_keywords = ['ARIMA', 'SAMIRA', 'LSTM', 'Conformal Prediction', 'multivariate time series', 'vector autoregression']\n",
        "Advanced_Methods_keywords = ['Transformer', 'GAN', 'EDAIN', 'MLP', 'deep learning', 'neural networks', 'supervised learning']\n",
        "\n",
        "# Function to analyze sentiment based on word lists\n",
        "def simple_sentiment_analysis(text):\n",
        "    # Tokenize the text by words and convert to lower case\n",
        "    words = text.lower().split()\n",
        "    # Count the number of Foundation and Advanced Methods words in the text\n",
        "    foundation_count = sum(words.count(word) == 1 for word in Foundation_keywords)\n",
        "    advanced_methods_count = sum(words.count(word) == 1 for word in Advanced_Methods_keywords)\n",
        "    # Determine the sentiment based on the counts\n",
        "    if foundation_count >= advanced_methods_count:\n",
        "        return 'Foundation'\n",
        "    elif advanced_methods_count <= foundation_count:\n",
        "        return 'Advanced Methods'\n",
        "    else:\n",
        "        return 'Mixed'\n",
        "\n",
        "# Load the sample CSV file into a DataFrame\n",
        "df = pd.read_csv('/content/sample_data/time_series_filtered.csv')\n",
        "\n",
        "# Apply the simple sentiment analysis to each summary\n",
        "df['sentiment_interpretation'] = df['summary'].apply(simple_sentiment_analysis)\n",
        "\n",
        "# Show the DataFrame with the sentiment analysis\n",
        "df[['date_published', 'summary', 'sentiment_interpretation']]"
      ],
      "metadata": {
        "id": "O8pjIl5W9ORH",
        "outputId": "2ccb8e75-55e6-45db-fd3e-fb170682fe7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3047
        }
      },
      "id": "O8pjIl5W9ORH",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 date_published  \\\n",
              "0     2023-11-06 17:52:08+00:00   \n",
              "1     2023-11-05 04:21:42+00:00   \n",
              "2     2023-11-04 02:07:47+00:00   \n",
              "3     2023-11-02 17:58:09+00:00   \n",
              "4     2023-11-02 17:55:41+00:00   \n",
              "...                         ...   \n",
              "2435  2006-01-04 16:29:30+00:00   \n",
              "2436  2005-08-16 10:53:52+00:00   \n",
              "2437  2005-05-25 04:46:35+00:00   \n",
              "2438  2005-05-10 10:01:59+00:00   \n",
              "2439  2004-12-01 16:32:49+00:00   \n",
              "\n",
              "                                                summary  \\\n",
              "0     While current generative models have achieved ...   \n",
              "1     Time series classification is a widely studied...   \n",
              "2     Meta learning is a promising technique for sol...   \n",
              "3     Diffusion models have been recently used for a...   \n",
              "4     Deep learning models, particularly Transformer...   \n",
              "...                                                 ...   \n",
              "2435  We consider a time series $X=\\{X_k, k\\in\\mathb...   \n",
              "2436  We consider a time series model involving a fr...   \n",
              "2437  zoo is an R package providing an S3 class with...   \n",
              "2438  We consider the prediction problem of a contin...   \n",
              "2439  For the last years, time-series mining has bec...   \n",
              "\n",
              "     sentiment_interpretation  \n",
              "0                  Foundation  \n",
              "1                  Foundation  \n",
              "2                  Foundation  \n",
              "3                  Foundation  \n",
              "4                  Foundation  \n",
              "...                       ...  \n",
              "2435               Foundation  \n",
              "2436               Foundation  \n",
              "2437               Foundation  \n",
              "2438               Foundation  \n",
              "2439               Foundation  \n",
              "\n",
              "[2440 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f6354456-a00c-438e-908d-f1fc2e032000\" 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",
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              "\n",
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          },
          "metadata": {},
          "execution_count": 43
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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import re\n",
        "\n",
        "# Sample lists of Foundation and Advanced Methods in lowercase for case-insensitive comparison\n",
        "Foundation_keywords = ['arima', 'samira', 'lstm', 'conformal prediction', 'multivariate time series', 'vector autoregression']\n",
        "Advanced_Methods_keywords = ['transformer', 'gan', 'edain', 'mlp', 'deep learning', 'neural networks', 'supervised learning']\n",
        "\n",
        "# Function to count occurrences of keywords based on word lists\n",
        "def count_keyword_occurrences(text):\n",
        "    # Use regular expressions to split the text into words and convert to lower case\n",
        "    words = re.findall(r'\\b\\w+\\b', text.lower())\n",
        "    # Count the number of distinct Foundation and Advanced Methods words in the text\n",
        "    foundation_count = sum(word in Foundation_keywords for word in words)\n",
        "    advanced_methods_count = sum(word in Advanced_Methods_keywords for word in words)\n",
        "    return foundation_count, advanced_methods_count\n",
        "\n",
        "# Load the sample CSV file into a DataFrame\n",
        "df = pd.read_csv('/content/sample_data/time_series_filtered.csv')\n",
        "\n",
        "# Apply the count_keyword_occurrences function to each summary and split the results into two columns\n",
        "df[['foundation_count', 'advanced_methods_count']] = df.apply(\n",
        "    lambda row: pd.Series(count_keyword_occurrences(row['summary'])),\n",
        "    axis=1\n",
        ")\n",
        "\n",
        "# Ensure the 'date_published' column is present or adjust the column name as necessary\n",
        "# Show the DataFrame with the counts\n",
        "df[['date_published', 'summary', 'foundation_count', 'advanced_methods_count']]"
      ],
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        "id": "5XwWAhwDVSVr",
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      "id": "5XwWAhwDVSVr",
      "execution_count": 3,
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              "                 date_published  \\\n",
              "0     2023-11-06 17:52:08+00:00   \n",
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              "2439  2004-12-01 16:32:49+00:00   \n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2a51636a-a8ea-467b-bb94-2124ac17857a 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-2a51636a-a8ea-467b-bb94-2124ac17857a');\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-049c102a-f1c7-4217-8f61-f2e1e1fdad7f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-049c102a-f1c7-4217-8f61-f2e1e1fdad7f')\"\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-049c102a-f1c7-4217-8f61-f2e1e1fdad7f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Count the occurrences of each 'foundation_count' value excluding zeros\n",
        "foundation_count_values = df['foundation_count'].value_counts().sort_index()\n",
        "foundation_count_values = foundation_count_values[foundation_count_values.index != 0]\n",
        "\n",
        "# Create a bar chart without the zero count\n",
        "plt.figure(figsize=(10, 6))\n",
        "foundation_count_values.plot(kind='bar', color='skyblue')\n",
        "plt.title('Foundation Count Frequency (Excluding Zero Count)')\n",
        "plt.xlabel('Foundation Count')\n",
        "plt.ylabel('Frequency')\n",
        "plt.xticks(rotation=0)  # To keep the x-axis labels horizontal\n",
        "plt.tight_layout()  # Adjust the plot to ensure everything fits without overlapping\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "U-G_P5vpWuZz",
        "outputId": "a2e8f20e-92c4-4c36-fc3a-ef9215b1e2be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        }
      },
      "id": "U-G_P5vpWuZz",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Count the occurrences of each 'advanced_methods_count' value excluding zeros\n",
        "advanced_methods_count_values = df['advanced_methods_count'].value_counts().sort_index()\n",
        "advanced_methods_count_values = advanced_methods_count_values[advanced_methods_count_values.index != 0]\n",
        "\n",
        "# Create a bar chart without the zero count for advanced methods\n",
        "plt.figure(figsize=(10, 6))\n",
        "advanced_methods_count_values.plot(kind='bar', color='coral')\n",
        "plt.title('Advanced Methods Count Frequency (Excluding Zero Count)')\n",
        "plt.xlabel('Advanced Methods Count')\n",
        "plt.ylabel('Frequency')\n",
        "plt.xticks(rotation=0)  # To keep the x-axis labels horizontal\n",
        "plt.tight_layout()  # Adjust the plot to ensure everything fits without overlapping\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "QJwshJAqXcdv",
        "outputId": "23ea30ec-c6b1-44f2-da06-193fd46f1b71",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        }
      },
      "id": "QJwshJAqXcdv",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
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