Extract_Comments_from_TrustPilot.ipynb

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        "<a href=\"https://colab.research.google.com/github/analyticswithadam/Python/blob/main/Extract_Comments_from_TrustPilot.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "from time import sleep\n",
        "import requests\n",
        "from google.colab import files\n",
        "import pandas as pd\n",
        "from bs4 import BeautifulSoup\n",
        "\n",
        "def soup2list(src, list_, attr=None):\n",
        "    if attr:\n",
        "        for val in src:\n",
        "            list_.append(val[attr])\n",
        "    else:\n",
        "        for val in src:\n",
        "            list_.append(val.get_text())\n",
        "\n",
        "users = []\n",
        "#userReviewNum = []\n",
        "ratings = []\n",
        "locations = []\n",
        "dates = []\n",
        "reviews = []\n",
        "\n",
        "from_page = 1\n",
        "to_page = 10\n",
        "company = 'guineys.ie'\n",
        "\n",
        "for i in range(from_page, to_page+1):\n",
        "\n",
        "   result = requests.get(fr\"https://www.trustpilot.com/review/{company}?page={i}\")\n",
        "   soup = BeautifulSoup(result.content)\n",
        "\n",
        "   # Trust Pilot was setup in a way that's not friendly to scraping, so this hacky method will do.\n",
        "   soup2list(soup.find_all('span', {'class','typography_heading-xxs__QKBS8 typography_appearance-default__AAY17'}), users)\n",
        "   soup2list(soup.find_all('div', {'class','typography_body-m__xgxZ_ typography_appearance-subtle__8_H2l styles_detailsIcon__Fo_ua'}), locations)\n",
        "   #soup2list(soup.find_all('span', {'class','typography_body-m__xgxZ_ typography_appearance-subtle__8_H2l'}), userReviewNum)\n",
        "   soup2list(soup.find_all('div', {'class','styles_reviewHeader__iU9Px'}), dates)\n",
        "   soup2list(soup.find_all('div', {'class','styles_reviewHeader__iU9Px'}), ratings, attr='data-service-review-rating')\n",
        "   soup2list(soup.find_all('div', {'class','styles_reviewContent__0Q2Tg'}), reviews)\n",
        "\n",
        "   # To avoid throttling\n",
        "   sleep(1)\n",
        "\n",
        "\n",
        "\n",
        "review_data = pd.DataFrame(\n",
        "{\n",
        "   'Username':users,\n",
        "   #'Total reviews':userReviewNum,\n",
        "   'location':locations,\n",
        "   'date':dates,\n",
        "   'content':reviews,\n",
        "   'Rating': ratings\n",
        "})\n"
      ]
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        "review_data"
      ],
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Username location                  date  \\\n",
              "0         Anna Mcmanus       IE    5 days agoVerified   \n",
              "1         Marie Quirke       IE  Jun 29, 2024Verified   \n",
              "2      Margaret Murphy       IE    5 days agoVerified   \n",
              "3                 Kate       IE    7 days agoVerified   \n",
              "4          Mary Clarke       GB  Jun 27, 2024Verified   \n",
              "..                 ...      ...                   ...   \n",
              "195           R Stokes       IE  May 22, 2024Verified   \n",
              "196              Guest       IE  May 30, 2024Verified   \n",
              "197     Kathleen Burke       IE  May 23, 2024Verified   \n",
              "198          Happymum        IE  May 21, 2024Verified   \n",
              "199  Ms Aisling Cahill       IE  May 27, 2024Verified   \n",
              "\n",
              "                                               content Rating  \n",
              "0    Received my DuveReceived my Duvet sets 2 days ...      5  \n",
              "1    Ordered my item late on Friday eveningOrdered ...      5  \n",
              "2    Bought two pairs of shoes fory MumBought two p...      5  \n",
              "3    Do not accept returnsDo not accept returns, op...      1  \n",
              "4    I have shopped with Guineys for over 50 years....      5  \n",
              "..                                                 ...    ...  \n",
              "195  Great delivery serviceDate of experience: May ...      5  \n",
              "196  Fast DeliveryFast DeliveryDate of experience: ...      5  \n",
              "197  Fast deliveryFast delivery Date of experience:...      5  \n",
              "198     Fast delivery Date of experience: May 13, 2024      5  \n",
              "199  Fast deliveryFast delivery Date of experience:...      4  \n",
              "\n",
              "[200 rows x 5 columns]"
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              "      <td>Anna Mcmanus</td>\n",
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              "      <td>Marie Quirke</td>\n",
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              "      <td>IE</td>\n",
              "      <td>May 22, 2024Verified</td>\n",
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              "      <td>5</td>\n",
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              "      <td>Kathleen Burke</td>\n",
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              "      <td>May 23, 2024Verified</td>\n",
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              "      <td>5</td>\n",
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              "      <td>Happymum</td>\n",
              "      <td>IE</td>\n",
              "      <td>May 21, 2024Verified</td>\n",
              "      <td>Fast delivery Date of experience: May 13, 2024</td>\n",
              "      <td>5</td>\n",
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              "      <th>199</th>\n",
              "      <td>Ms Aisling Cahill</td>\n",
              "      <td>IE</td>\n",
              "      <td>May 27, 2024Verified</td>\n",
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              "      <td>4</td>\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('review_data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "review_data",
              "summary": "{\n  \"name\": \"review_data\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"Username\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 164,\n        \"samples\": [\n          \"Mary Hunter\",\n          \"Mrs Caroline Healy\",\n          \"Vanessa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"GB\",\n          \"GR\",\n          \"IE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 49,\n        \"samples\": [\n          \"Jun 5, 2024Verified\",\n          \"Jun 15, 2024Verified\",\n          \"May 20, 2024Verified\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"content\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 200,\n        \"samples\": [\n          \"Fast service and ...Fast service , packed well and item as described ...Date of experience: June 10, 2024\",\n          \"First time buying from Guineys and it\\u2026First time buying from Guineys and it was fantastic. Great prices, huge selection and quick delivery. Highly recommended.Date of experience: June 19, 2024\",\n          \"Quick Delivery - Perfect CutQuick delivery. Great range of fabric for all projects and budgets. Fabric cut to perfection! I've had some bad experiences from fabric stores where it's crooked or short etc. This was perfect.Date of experience: May 24, 2024\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"1\",\n          \"4\",\n          \"3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert the DataFrame to a CSV file and download it\n",
        "review_data.to_csv('reviews.csv', index=False)\n",
        "files.download('reviews.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "XfeWnNaSVd02",
        "outputId": "0fe72260-bc73-4c81-f931-2aedb6665fd4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_1671fb94-ed1a-4bff-a6d2-410cd20df03f\", \"reviews.csv\", 36916)"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}