{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "4qF4ZVcHA90K"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from pytrends.request import TrendReq\n",
        "import matplotlib.pyplot as plt\n",
        "import time\n",
        "\n",
        "Trending_topics = TrendReq(hl='en-US', tz=360)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kw_list=[\"Cloud Computing\"]\n",
        "Trending_topics.build_payload(kw_list,cat=0, timeframe='today 12-m')\n",
        "time.sleep(5)"
      ],
      "metadata": {
        "id": "-2VkoPPZEBQ7"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = Trending_topics.interest_over_time()\n",
        "data = data.sort_values(by=\"Cloud Computing\", ascending = False)\n",
        "data = data.head(10)\n",
        "print(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sI-eYgxkEPXa",
        "outputId": "4f370166-6a78-4e9b-d0cc-72f06e31d634"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            Cloud Computing  isPartial\n",
            "date                                  \n",
            "2024-05-19              100      False\n",
            "2024-11-17              100      False\n",
            "2024-12-01               99      False\n",
            "2024-05-05               97      False\n",
            "2025-01-19               97      False\n",
            "2025-04-20               94      False\n",
            "2024-09-22               94      False\n",
            "2024-11-10               93      False\n",
            "2024-11-24               93      False\n",
            "2024-10-20               92      False\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/pytrends/request.py:260: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
            "  df = df.fillna(False)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kw_list = [\"Cloud Computing\"]\n",
        "Trending_topics.build_payload(kw_list, cat=0, timeframe='2024-01-01 2024-02-01', geo='', gprop='')\n",
        "data = Trending_topics.interest_over_time()\n",
        "data = data.sort_values(by=\"Cloud Computing\", ascending = False)\n",
        "data = data.head(10)\n",
        "print(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rdjzVNfpET4W",
        "outputId": "8f7140d2-66fc-4b71-bfac-2a6c65693614"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            Cloud Computing  isPartial\n",
            "date                                  \n",
            "2024-01-29              100      False\n",
            "2024-01-03               98      False\n",
            "2024-01-10               87      False\n",
            "2024-01-30               85      False\n",
            "2024-01-23               83      False\n",
            "2024-01-27               81      False\n",
            "2024-01-09               80      False\n",
            "2024-01-24               80      False\n",
            "2024-01-08               79      False\n",
            "2024-01-31               78      False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = Trending_topics.interest_by_region()\n",
        "data = data.sort_values(by=\"Cloud Computing\",\n",
        "                        ascending = False)\n",
        "data = data.head(10)\n",
        "print(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G1UhcdusEWog",
        "outputId": "f07650a3-c195-4507-b5c9-4960eb60e883"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            Cloud Computing\n",
            "geoName                    \n",
            "Nepal                   100\n",
            "India                    81\n",
            "Zimbabwe                 79\n",
            "Ethiopia                 76\n",
            "St. Helena               62\n",
            "Ghana                    60\n",
            "Sri Lanka                58\n",
            "Cameroon                 58\n",
            "Kenya                    53\n",
            "Nigeria                  45\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.reset_index().plot(x='geoName', y='Cloud Computing',\n",
        "                        figsize=(10,5), kind=\"bar\")\n",
        "plt.style.use('fivethirtyeight')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "bzhZ2k4fEeXi",
        "outputId": "157f0f74-98d6-4d83-b85e-b3cf931e8a33"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "try:\n",
        "    Trending_topics.build_payload(kw_list=[''])\n",
        "    related_queries = Trending_topics.related_queries()\n",
        "    related_queries.values()\n",
        "except (KeyError, IndexError):\n",
        "    print(\"No related queries found for 'Cloud Computing'\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-BxnRZ8XEygX",
        "outputId": "3b74d4d2-c0f6-4bb5-86a4-aa6ba67784fc"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No related queries found for 'Cloud Computing'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "keywords = Trending_topics.suggestions(\n",
        "  keyword='Cloud Computing')\n",
        "df = pd.DataFrame(keywords)\n",
        "df.drop(columns= 'mid')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ZOZEyGGlHc1h",
        "outputId": "88cbcc7f-c951-494c-cb2a-603f69ea0845"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                    title                          type\n",
              "0                         Cloud computing                         Topic\n",
              "1                Cloud computing security                         Topic\n",
              "2                         Cloud computing                         Topic\n",
              "3                           Alibaba Cloud  Computer application company\n",
              "4  Private cloud computing infrastructure                         Topic"
            ],
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "        document.querySelector('#df-b86c2a19-b67d-4eba-aead-4fc370e4d5b8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "        const element = document.querySelector('#df-b86c2a19-b67d-4eba-aead-4fc370e4d5b8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "  .colab-df-quickchart {\n",
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              "  }\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-7b1b2de2-5dbe-4f0d-8c6e-7fd2603aa517 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Cloud computing security\",\n          \"Private cloud computing infrastructure\",\n          \"Cloud computing\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Computer application company\",\n          \"Topic\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0gV3fjDVKGW4"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}