british_airways_data_scraping.ipynb

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      "metadata": {
        "id": "SDsambMschIM"
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      "outputs": [],
      "source": [
        "import requests\n",
        "from bs4 import BeautifulSoup\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ayvGvfLGchIN",
        "outputId": "53643752-0a04-4887-80f3-1c66af238cb2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scraping page 1\n",
            "   ---> 100 total reviews\n",
            "   ---> 100 total header\n",
            "   ---> 101 total rating\n",
            "Scraping page 2\n",
            "   ---> 200 total reviews\n",
            "   ---> 200 total header\n",
            "   ---> 202 total rating\n",
            "Scraping page 3\n",
            "   ---> 300 total reviews\n",
            "   ---> 300 total header\n",
            "   ---> 303 total rating\n",
            "Scraping page 4\n",
            "   ---> 400 total reviews\n",
            "   ---> 400 total header\n",
            "   ---> 404 total rating\n",
            "Scraping page 5\n",
            "   ---> 500 total reviews\n",
            "   ---> 500 total header\n",
            "   ---> 505 total rating\n",
            "Scraping page 6\n",
            "   ---> 600 total reviews\n",
            "   ---> 600 total header\n",
            "   ---> 606 total rating\n",
            "Scraping page 7\n",
            "   ---> 700 total reviews\n",
            "   ---> 700 total header\n",
            "   ---> 707 total rating\n",
            "Scraping page 8\n",
            "   ---> 800 total reviews\n",
            "   ---> 800 total header\n",
            "   ---> 808 total rating\n",
            "Scraping page 9\n",
            "   ---> 900 total reviews\n",
            "   ---> 900 total header\n",
            "   ---> 909 total rating\n",
            "Scraping page 10\n",
            "   ---> 1000 total reviews\n",
            "   ---> 1000 total header\n",
            "   ---> 1010 total rating\n",
            "Scraping page 11\n",
            "   ---> 1100 total reviews\n",
            "   ---> 1100 total header\n",
            "   ---> 1111 total rating\n",
            "Scraping page 12\n",
            "   ---> 1200 total reviews\n",
            "   ---> 1200 total header\n",
            "   ---> 1212 total rating\n",
            "Scraping page 13\n",
            "   ---> 1300 total reviews\n",
            "   ---> 1300 total header\n",
            "   ---> 1313 total rating\n",
            "Scraping page 14\n",
            "   ---> 1400 total reviews\n",
            "   ---> 1400 total header\n",
            "   ---> 1414 total rating\n",
            "Scraping page 15\n",
            "   ---> 1500 total reviews\n",
            "   ---> 1500 total header\n",
            "   ---> 1515 total rating\n",
            "Scraping page 16\n",
            "   ---> 1600 total reviews\n",
            "   ---> 1600 total header\n",
            "   ---> 1616 total rating\n",
            "Scraping page 17\n",
            "   ---> 1700 total reviews\n",
            "   ---> 1700 total header\n",
            "   ---> 1717 total rating\n",
            "Scraping page 18\n",
            "   ---> 1800 total reviews\n",
            "   ---> 1800 total header\n",
            "   ---> 1818 total rating\n",
            "Scraping page 19\n",
            "   ---> 1900 total reviews\n",
            "   ---> 1900 total header\n",
            "   ---> 1919 total rating\n",
            "Scraping page 20\n",
            "   ---> 2000 total reviews\n",
            "   ---> 2000 total header\n",
            "   ---> 2020 total rating\n",
            "Scraping page 21\n",
            "   ---> 2100 total reviews\n",
            "   ---> 2100 total header\n",
            "   ---> 2121 total rating\n",
            "Scraping page 22\n",
            "   ---> 2200 total reviews\n",
            "   ---> 2200 total header\n",
            "   ---> 2222 total rating\n",
            "Scraping page 23\n",
            "   ---> 2300 total reviews\n",
            "   ---> 2300 total header\n",
            "   ---> 2323 total rating\n",
            "Scraping page 24\n",
            "   ---> 2400 total reviews\n",
            "   ---> 2400 total header\n",
            "   ---> 2424 total rating\n",
            "Scraping page 25\n",
            "   ---> 2500 total reviews\n",
            "   ---> 2500 total header\n",
            "   ---> 2525 total rating\n",
            "Scraping page 26\n",
            "   ---> 2600 total reviews\n",
            "   ---> 2600 total header\n",
            "   ---> 2626 total rating\n",
            "Scraping page 27\n",
            "   ---> 2700 total reviews\n",
            "   ---> 2700 total header\n",
            "   ---> 2727 total rating\n",
            "Scraping page 28\n",
            "   ---> 2800 total reviews\n",
            "   ---> 2800 total header\n",
            "   ---> 2828 total rating\n",
            "Scraping page 29\n",
            "   ---> 2900 total reviews\n",
            "   ---> 2900 total header\n",
            "   ---> 2929 total rating\n",
            "Scraping page 30\n",
            "   ---> 3000 total reviews\n",
            "   ---> 3000 total header\n",
            "   ---> 3029 total rating\n",
            "Scraping page 31\n",
            "   ---> 3100 total reviews\n",
            "   ---> 3100 total header\n",
            "   ---> 3130 total rating\n",
            "Scraping page 32\n",
            "   ---> 3200 total reviews\n",
            "   ---> 3200 total header\n",
            "   ---> 3229 total rating\n",
            "Scraping page 33\n",
            "   ---> 3300 total reviews\n",
            "   ---> 3300 total header\n",
            "   ---> 3330 total rating\n",
            "Scraping page 34\n",
            "   ---> 3400 total reviews\n",
            "   ---> 3400 total header\n",
            "   ---> 3429 total rating\n",
            "Scraping page 35\n",
            "   ---> 3500 total reviews\n",
            "   ---> 3500 total header\n",
            "   ---> 3530 total rating\n",
            "Scraping page 36\n",
            "   ---> 3556 total reviews\n",
            "   ---> 3556 total header\n",
            "   ---> 3587 total rating\n"
          ]
        }
      ],
      "source": [
        "base_url = \"https://www.airlinequality.com/airline-reviews/british-airways\"\n",
        "pages = 36\n",
        "page_size = 100\n",
        "\n",
        "\n",
        "\n",
        "header = []\n",
        "reviews = []\n",
        "rating = []\n",
        "\n",
        "star = []\n",
        "star_labes = []\n",
        "\n",
        "tables = []\n",
        "\n",
        "# for i in range(1, pages + 1):\n",
        "for i in range(1, pages + 1):\n",
        "\n",
        "    print(f\"Scraping page {i}\")\n",
        "\n",
        "    # Create URL to collect links from paginated data\n",
        "    url = f\"{base_url}/page/{i}/?sortby=post_date%3ADesc&pagesize={page_size}\"\n",
        "\n",
        "    # Collect HTML data from this page\n",
        "    response = requests.get(url)\n",
        "\n",
        "    # Read tables\n",
        "    tables.append(pd.read_html(response.text, index_col=0))\n",
        "\n",
        "    # Parse content\n",
        "    content = response.content\n",
        "    parsed_content = BeautifulSoup(content, 'html.parser')\n",
        "\n",
        "    for para in parsed_content.find_all(\"h2\", {\"class\": \"text_header\"}):\n",
        "        header.append(para.get_text())  \n",
        "\n",
        "    for para in parsed_content.find_all(\"div\", {\"class\": \"text_content\"}):\n",
        "        reviews.append(para.get_text())\n",
        "\n",
        "    for para in parsed_content.find_all(\"span\", {\"itemprop\": \"ratingValue\"}):\n",
        "        rating.append(para.get_text())\n",
        "\n",
        "\n",
        "    print(f\"   ---> {len(reviews)} total reviews\")\n",
        "    print(f\"   ---> {len(header)} total header\")\n",
        "    print(f\"   ---> {len(rating)} total rating\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rating_cleaned = []\n",
        "for i in rating:\n",
        "  if i != '\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t5':\n",
        "    rating_cleaned.append(i)"
      ],
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      "outputs": []
    },
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
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            "text/plain": [
              "                                              header  \\\n",
              "0  \"the worst airline experience I've ever had to...   \n",
              "1                                 \"Service was good\"   \n",
              "2             \"Last time I will fly British Airways\"   \n",
              "3                \"cancel your flight without notice\"   \n",
              "4                     \"flights changed with no cost\"   \n",
              "\n",
              "                                             reviews rating  \n",
              "0  Not Verified |  My itinerary was supposed to b...      1  \n",
              "1  ✅ Trip Verified |  Busy day at LHR and flight ...      9  \n",
              "2  ✅ Trip Verified | Worst seats I have ever enco...      2  \n",
              "3  Not Verified | Top Ten REASONS to not use Brit...      1  \n",
              "4  Not Verified |  Easy check in on the way to He...     10  "
            ],
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              "      <th>rating</th>\n",
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              "      <td>\"the worst airline experience I've ever had to...</td>\n",
              "      <td>Not Verified |  My itinerary was supposed to b...</td>\n",
              "      <td>1</td>\n",
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              "      <td>\"Service was good\"</td>\n",
              "      <td>✅ Trip Verified |  Busy day at LHR and flight ...</td>\n",
              "      <td>9</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>\"Last time I will fly British Airways\"</td>\n",
              "      <td>✅ Trip Verified | Worst seats I have ever enco...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>\"cancel your flight without notice\"</td>\n",
              "      <td>Not Verified | Top Ten REASONS to not use Brit...</td>\n",
              "      <td>1</td>\n",
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              "      <th>4</th>\n",
              "      <td>\"flights changed with no cost\"</td>\n",
              "      <td>Not Verified |  Easy check in on the way to He...</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "          element.innerHTML = '';\n",
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          },
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      ],
      "source": [
        "df = pd.DataFrame()\n",
        "df[\"header\"] = header\n",
        "df[\"reviews\"] = reviews\n",
        "df[\"rating\"] = rating_cleaned\n",
        "\n",
        "\n",
        "df.head()"
      ]
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      "cell_type": "code",
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        "tables[0][1].transpose()"
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      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0 Type Of Traveller      Seat Type                Route Date Flown  \\\n",
              "1      Solo Leisure  Economy Class  Las Vegas to Venice   May 2023   \n",
              "\n",
              "0 Seat Comfort Cabin Staff Service Food & Beverages Inflight Entertainment  \\\n",
              "1        12345               12345            12345                  12345   \n",
              "\n",
              "0 Ground Service Wifi & Connectivity Value For Money Recommended  \n",
              "1          12345               12345           12345          no  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Type Of Traveller</th>\n",
              "      <th>Seat Type</th>\n",
              "      <th>Route</th>\n",
              "      <th>Date Flown</th>\n",
              "      <th>Seat Comfort</th>\n",
              "      <th>Cabin Staff Service</th>\n",
              "      <th>Food &amp; Beverages</th>\n",
              "      <th>Inflight Entertainment</th>\n",
              "      <th>Ground Service</th>\n",
              "      <th>Wifi &amp; Connectivity</th>\n",
              "      <th>Value For Money</th>\n",
              "      <th>Recommended</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Solo Leisure</td>\n",
              "      <td>Economy Class</td>\n",
              "      <td>Las Vegas to Venice</td>\n",
              "      <td>May 2023</td>\n",
              "      <td>12345</td>\n",
              "      <td>12345</td>\n",
              "      <td>12345</td>\n",
              "      <td>12345</td>\n",
              "      <td>12345</td>\n",
              "      <td>12345</td>\n",
              "      <td>12345</td>\n",
              "      <td>no</td>\n",
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              "  </tbody>\n",
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              "          document.querySelector('#df-b26164c6-f2f2-4521-9e63-5c47c674ef70 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b26164c6-f2f2-4521-9e63-5c47c674ef70');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "flat_list = []\n",
        "for sublist in tables:\n",
        "    for item in sublist:\n",
        "        flat_list.append(item)"
      ],
      "metadata": {
        "id": "c8I-YPjoUWeZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# List to store transposed tables\n",
        "transposed_tables = []\n",
        "\n",
        "# Iterate over the tables and transpose them\n",
        "for table in flat_list:\n",
        "    transposed_tables.append(table.transpose())\n",
        "\n",
        "# Concatenate the transposed tables along axis 0\n",
        "result = pd.concat(transposed_tables, axis=0)"
      ],
      "metadata": {
        "id": "O2VrYwTamuHg"
      },
      "execution_count": null,
      "outputs": []
    },
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        "result"
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        "id": "MKD1rGYOR9St",
        "outputId": "9f9f639b-2fa6-4b2c-aa6d-b7f9f497a3c3"
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      "execution_count": null,
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0e339529-64eb-4d43-a00e-8807c83f4e83');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results2 = result.dropna(subset=[\"Route\", \"Date Flown\", \"Cabin Staff Service\", \"Ground Service\", \"Wifi & Connectivity\", \"Value For Money\"], how='all')\n",
        "results3 = results2.drop(columns = [\"Food & Beverages\", \"Inflight Entertainment\", \n",
        "                                    \"Seat Comfort\", \"Staff Service\", \"Value for Money\",\n",
        "                                    \"Cabin Staff Service\", \"Wifi & Connectivity\", \"Ground Service\", \"Value For Money\"])\n",
        "results4 = results3.reset_index()\n",
        "results4 = results4.drop(columns = \"index\")\n",
        "results4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "VD6sJFjZVLzG",
        "outputId": "bd88a549-f9de-4117-9969-ba3b1c5ffb8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0   Type Of Traveller        Seat Type                          Route  \\\n",
              "0        Solo Leisure    Economy Class            Las Vegas to Venice   \n",
              "1      Family Leisure  Premium Economy             London to New York   \n",
              "2      Couple Leisure    Economy Class               Lisbon to London   \n",
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              "..                ...              ...                            ...   \n",
              "995          Business   Business Class            London to Frankfurt   \n",
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              "997    Couple Leisure   Business Class             Bermuda to Gatwick   \n",
              "998          Business    Economy Class  Aberdeen to Boston via London   \n",
              "999      Solo Leisure    Economy Class              London to Hamburg   \n",
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              "3      May 2023          no             NaN  \n",
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              "..          ...         ...             ...  \n",
              "995   July 2018          no             NaN  \n",
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              "997    May 2018         yes  Boeing 777-200  \n",
              "998   July 2018         yes             NaN  \n",
              "999   June 2018         yes            A319  \n",
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              "[1000 rows x 6 columns]"
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Type Of Traveller</th>\n",
              "      <th>Seat Type</th>\n",
              "      <th>Route</th>\n",
              "      <th>Date Flown</th>\n",
              "      <th>Recommended</th>\n",
              "      <th>Aircraft</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Solo Leisure</td>\n",
              "      <td>Economy Class</td>\n",
              "      <td>Las Vegas to Venice</td>\n",
              "      <td>May 2023</td>\n",
              "      <td>no</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Family Leisure</td>\n",
              "      <td>Premium Economy</td>\n",
              "      <td>London to New York</td>\n",
              "      <td>May 2023</td>\n",
              "      <td>yes</td>\n",
              "      <td>Boeing 777-300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Couple Leisure</td>\n",
              "      <td>Economy Class</td>\n",
              "      <td>Lisbon to London</td>\n",
              "      <td>May 2023</td>\n",
              "      <td>no</td>\n",
              "      <td>A320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Couple Leisure</td>\n",
              "      <td>Premium Economy</td>\n",
              "      <td>Dallas to Madrid via London</td>\n",
              "      <td>May 2023</td>\n",
              "      <td>no</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Couple Leisure</td>\n",
              "      <td>Economy Class</td>\n",
              "      <td>London to Valencia</td>\n",
              "      <td>March 2023</td>\n",
              "      <td>yes</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>Business</td>\n",
              "      <td>Business Class</td>\n",
              "      <td>London to Frankfurt</td>\n",
              "      <td>July 2018</td>\n",
              "      <td>no</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>Solo Leisure</td>\n",
              "      <td>Business Class</td>\n",
              "      <td>Gatwick to Malaga</td>\n",
              "      <td>July 2018</td>\n",
              "      <td>yes</td>\n",
              "      <td>A321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>Couple Leisure</td>\n",
              "      <td>Business Class</td>\n",
              "      <td>Bermuda to Gatwick</td>\n",
              "      <td>May 2018</td>\n",
              "      <td>yes</td>\n",
              "      <td>Boeing 777-200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>Business</td>\n",
              "      <td>Economy Class</td>\n",
              "      <td>Aberdeen to Boston via London</td>\n",
              "      <td>July 2018</td>\n",
              "      <td>yes</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>Solo Leisure</td>\n",
              "      <td>Economy Class</td>\n",
              "      <td>London to Hamburg</td>\n",
              "      <td>June 2018</td>\n",
              "      <td>yes</td>\n",
              "      <td>A319</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8e43d676-0835-4393-8c64-aee7dc5a0a3c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\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",
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              "\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8e43d676-0835-4393-8c64-aee7dc5a0a3c 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-8e43d676-0835-4393-8c64-aee7dc5a0a3c');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df.join(results4)\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "ddTwto__avQJ",
        "outputId": "c55cd920-a695-41ff-a0a0-26fdba022176"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                header  \\\n",
              "0    \"the worst airline experience I've ever had to...   \n",
              "1                                   \"Service was good\"   \n",
              "2               \"Last time I will fly British Airways\"   \n",
              "3                  \"cancel your flight without notice\"   \n",
              "4                       \"flights changed with no cost\"   \n",
              "..                                                 ...   \n",
              "995              \"lounges in Heathrow are overcrowded\"   \n",
              "996                             \"was nicely impressed\"   \n",
              "997                 \"pole vault over passenger's legs\"   \n",
              "998                        \"seats weren't wide enough\"   \n",
              "999                       \"Seat was quite comfortable\"   \n",
              "\n",
              "                                               reviews rating  \\\n",
              "0    Not Verified |  My itinerary was supposed to b...      1   \n",
              "1    ✅ Trip Verified |  Busy day at LHR and flight ...      9   \n",
              "2    ✅ Trip Verified | Worst seats I have ever enco...      2   \n",
              "3    Not Verified | Top Ten REASONS to not use Brit...      1   \n",
              "4    Not Verified |  Easy check in on the way to He...     10   \n",
              "..                                                 ...    ...   \n",
              "995  ✅ Trip Verified |  London to Frankfurt. I need...      1   \n",
              "996  ✅ Trip Verified | Gatwick to Malaga. When book...      9   \n",
              "997  ✅ Trip Verified |  Bermuda to Gatwick. Usual w...      7   \n",
              "998  ✅ Trip Verified |  Aberdeen to Boston via Lond...      6   \n",
              "999  ✅ Trip Verified |  London to Hamburg. Baggage ...      8   \n",
              "\n",
              "    Type Of Traveller        Seat Type                          Route  \\\n",
              "0        Solo Leisure    Economy Class            Las Vegas to Venice   \n",
              "1      Family Leisure  Premium Economy             London to New York   \n",
              "2      Couple Leisure    Economy Class               Lisbon to London   \n",
              "3      Couple Leisure  Premium Economy    Dallas to Madrid via London   \n",
              "4      Couple Leisure    Economy Class             London to Valencia   \n",
              "..                ...              ...                            ...   \n",
              "995          Business   Business Class            London to Frankfurt   \n",
              "996      Solo Leisure   Business Class              Gatwick to Malaga   \n",
              "997    Couple Leisure   Business Class             Bermuda to Gatwick   \n",
              "998          Business    Economy Class  Aberdeen to Boston via London   \n",
              "999      Solo Leisure    Economy Class              London to Hamburg   \n",
              "\n",
              "     Date Flown Recommended        Aircraft  \n",
              "0      May 2023          no             NaN  \n",
              "1      May 2023         yes  Boeing 777-300  \n",
              "2      May 2023          no            A320  \n",
              "3      May 2023          no             NaN  \n",
              "4    March 2023         yes             NaN  \n",
              "..          ...         ...             ...  \n",
              "995   July 2018          no             NaN  \n",
              "996   July 2018         yes            A321  \n",
              "997    May 2018         yes  Boeing 777-200  \n",
              "998   July 2018         yes             NaN  \n",
              "999   June 2018         yes            A319  \n",
              "\n",
              "[1000 rows x 9 columns]"
            ],
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              "      <td>Not Verified | Top Ten REASONS to not use Brit...</td>\n",
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              "      <td>Premium Economy</td>\n",
              "      <td>Dallas to Madrid via London</td>\n",
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              "      <th>4</th>\n",
              "      <td>\"flights changed with no cost\"</td>\n",
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              "      <td>10</td>\n",
              "      <td>Couple Leisure</td>\n",
              "      <td>Economy Class</td>\n",
              "      <td>London to Valencia</td>\n",
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              "      <td>yes</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>995</th>\n",
              "      <td>\"lounges in Heathrow are overcrowded\"</td>\n",
              "      <td>✅ Trip Verified |  London to Frankfurt. I need...</td>\n",
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              "      <th>996</th>\n",
              "      <td>\"was nicely impressed\"</td>\n",
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              "      <td>7</td>\n",
              "      <td>Couple Leisure</td>\n",
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              "      <td>yes</td>\n",
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              "      <th>998</th>\n",
              "      <td>\"seats weren't wide enough\"</td>\n",
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              "      <td>6</td>\n",
              "      <td>Business</td>\n",
              "      <td>Economy Class</td>\n",
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              "      <td>July 2018</td>\n",
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              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>\"Seat was quite comfortable\"</td>\n",
              "      <td>✅ Trip Verified |  London to Hamburg. Baggage ...</td>\n",
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              "      <td>Economy Class</td>\n",
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              "      <td>yes</td>\n",
              "      <td>A319</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 9 columns</p>\n",
              "</div>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7c2245bb-18c5-46e6-875d-f10f1f958a6c');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Efdd2zLobndp",
        "outputId": "c07230f1-2ddf-447d-ef4d-551b1f6a0413"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "header                 0\n",
              "reviews                0\n",
              "rating                 0\n",
              "Type Of Traveller      1\n",
              "Seat Type              0\n",
              "Route                  3\n",
              "Date Flown             0\n",
              "Recommended            0\n",
              "Aircraft             429\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-uUX8UdIchIO"
      },
      "outputs": [],
      "source": [
        "df2.to_csv(\"BA_reviews.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing and Cleaning Text\n",
        "\n",
        "df2[\"reviews\"] = df2[\"reviews\"].str.split(\"|\").str[1]\n",
        "df2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "jo37bL0t9mCM",
        "outputId": "48993e4a-a91b-4c01-9024-1aceb26a4c39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              header  \\\n",
              "0  \"the worst airline experience I've ever had to...   \n",
              "1                                 \"Service was good\"   \n",
              "2             \"Last time I will fly British Airways\"   \n",
              "3                \"cancel your flight without notice\"   \n",
              "4                     \"flights changed with no cost\"   \n",
              "\n",
              "                                             reviews rating Type Of Traveller  \\\n",
              "0    My itinerary was supposed to be Las Vegas-Ch...      1      Solo Leisure   \n",
              "1    Busy day at LHR and flight full. Lounge crow...      9    Family Leisure   \n",
              "2   Worst seats I have ever encountered in econom...      2    Couple Leisure   \n",
              "3   Top Ten REASONS to not use British Airways To...      1    Couple Leisure   \n",
              "4    Easy check in on the way to Heathrow. The fl...     10    Couple Leisure   \n",
              "\n",
              "         Seat Type                        Route  Date Flown Recommended  \\\n",
              "0    Economy Class          Las Vegas to Venice    May 2023          no   \n",
              "1  Premium Economy           London to New York    May 2023         yes   \n",
              "2    Economy Class             Lisbon to London    May 2023          no   \n",
              "3  Premium Economy  Dallas to Madrid via London    May 2023          no   \n",
              "4    Economy Class           London to Valencia  March 2023         yes   \n",
              "\n",
              "         Aircraft  \n",
              "0             NaN  \n",
              "1  Boeing 777-300  \n",
              "2            A320  \n",
              "3             NaN  \n",
              "4             NaN  "
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 144
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2[\"Recommended\"].value_counts()/len(df2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z_iQ957OdZY1",
        "outputId": "d15467a7-c7e8-4d65-976a-b59e0b7302cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "no     0.639\n",
              "yes    0.361\n",
              "Name: Recommended, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Scraping data"
      ],
      "metadata": {
        "id": "fATW4co7gk3g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from bs4 import BeautifulSoup\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import requests as rq\n",
        "\n",
        "global rating, Aircraft, Class, Travel_type, Route, Date, comfort, staff, food, entertainment, wifi, ground_service, vfm, recommend\n",
        "\n",
        "verified=[]\n",
        "rating=[]\n",
        "feedback=[]\n",
        "Aircraft=[]\n",
        "Class=[]\n",
        "Travel_type=[]\n",
        "Route=[]\n",
        "Date=[]\n",
        "comfort=[]\n",
        "staff=[]\n",
        "food=[]\n",
        "entertainment=[]\n",
        "wifi=[]\n",
        "ground_service=[]\n",
        "vfm=[]\n",
        "recommend=[]\n",
        "\n",
        "\n",
        "def value_fetcher(soup):\n",
        "    rating.append(np.nan)\n",
        "    Aircraft.append(np.nan)\n",
        "    Class.append(np.nan)\n",
        "    Travel_type.append(np.nan)\n",
        "    Route.append(np.nan)\n",
        "    Date.append(np.nan)\n",
        "    comfort.append(np.nan)\n",
        "    staff.append(np.nan)\n",
        "    food.append(np.nan)\n",
        "    entertainment.append(np.nan)\n",
        "    wifi.append(np.nan)\n",
        "    ground_service.append(np.nan)\n",
        "    vfm.append(np.nan)\n",
        "    recommend.append(np.nan)\n",
        "    \n",
        "    for row in soup.find_all('tr'):\n",
        "        header=row.find('td',class_='review-rating-header').text\n",
        "        value=row.find('td',class_='review-value')\n",
        "        if header=='Aircraft':\n",
        "            Aircraft[-1]=value.text\n",
        "        elif header=='Type Of Traveller':\n",
        "            Travel_type[-1]=value.text\n",
        "        elif header=='Seat Type':\n",
        "            Class[-1]=value.text\n",
        "        elif header=='Route':\n",
        "            Route[-1]=value.text\n",
        "        elif header=='Date Flown':\n",
        "            Date[-1]=value.text\n",
        "        elif header=='Seat Comfort':\n",
        "            comfort[-1]=len(row.find_all('span',class_='star fill'))\n",
        "        elif header=='Cabin Staff Service':\n",
        "            staff[-1]=len(row.find_all('span',class_='star fill'))\n",
        "        elif header=='Food & Beverages':\n",
        "            food[-1]=len(row.find_all('span',class_='star fill'))\n",
        "        elif header=='Inflight Entertainment':\n",
        "            entertainment[-1]=len(row.find_all('span',class_='star fill'))\n",
        "        elif header=='Ground Service':\n",
        "            ground_service[-1]=len(row.find_all('span',class_='star fill'))\n",
        "        elif header=='Value For Money':\n",
        "            vfm[-1]=len(row.find_all('span',class_='star fill'))\n",
        "        elif header=='Recommended':\n",
        "            recommend[-1]=value.text\n",
        "        elif header=='Wifi & Connectivity':\n",
        "            wifi[-1]=len(row.find_all('span',class_='star fill'))\n",
        "\n",
        "\n",
        "def fetcher(soup,tag,key,value):\n",
        "    if soup.find(tag,{key:value}):\n",
        "        return soup.find(tag,{key:value}).text\n",
        "    return np.nan\n",
        "\n",
        "\n",
        "for i in range(1,344):\n",
        "    url=f'https://www.airlinequality.com/airline-reviews/british-airways/page/{i}/'\n",
        "    page=rq.get(url).text\n",
        "    soup=BeautifulSoup(page,'lxml')\n",
        "    articles= soup.find_all('article',{'itemprop':'review'})\n",
        "    for article in articles:\n",
        "        rating.append(fetcher(article,'span','itemprop','ratingValue'))\n",
        "        div=article.find('div',class_='tc_mobile')\n",
        "        content=(div.find('div',class_='text_content').text.split('|'))\n",
        "        if len(content)==2:\n",
        "            verification,review=content\n",
        "        else:\n",
        "            verification =np.nan\n",
        "            review=content[0]\n",
        "        verified.append(verification)\n",
        "        feedback.append(review)\n",
        "        table=div.find('table',class_='review-ratings')\n",
        "        value_fetcher(table)\n"
      ],
      "metadata": {
        "id": "FVfkeBE6doR3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.DataFrame({'status':verified,'aircraft':Aircraft,'travel_type':Travel_type,'travel_class':Class,'route':Route,'date':Date,'seating_comfort':comfort,'staff_service':staff,'food_quality':food,'entertainment':entertainment,'wifi':wifi,'ground_service':ground_service,'value_for_money':vfm,'recommended':recommend,'overall_rating':rating[::2],'review':feedback})"
      ],
      "metadata": {
        "id": "7FXD18q9gCkS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv(\"BA_reviews2.csv\")"
      ],
      "metadata": {
        "id": "4V0IOSp9jUOs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://www.kaggle.com/code/minnikeswarrao/web-scraping-on-skytrax-com/notebook for the web scraping tool"
      ],
      "metadata": {
        "id": "4G2RqJPPhYW5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "JEfHSfuGgEnv",
        "outputId": "65fde3e4-1d3f-4043-b12b-d0d37cc0724f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             status        aircraft     travel_type     travel_class  \\\n",
              "0     Not Verified              NaN    Solo Leisure    Economy Class   \n",
              "1  ✅ Trip Verified   Boeing 777-300  Family Leisure  Premium Economy   \n",
              "2  ✅ Trip Verified             A320  Couple Leisure    Economy Class   \n",
              "3     Not Verified              NaN  Couple Leisure  Premium Economy   \n",
              "4     Not Verified              NaN  Couple Leisure    Economy Class   \n",
              "\n",
              "                         route        date  seating_comfort  staff_service  \\\n",
              "0          Las Vegas to Venice    May 2023              1.0            1.0   \n",
              "1           London to New York    May 2023              4.0            4.0   \n",
              "2            Lisbon to London     May 2023              1.0            3.0   \n",
              "3  Dallas to Madrid via London    May 2023              1.0            1.0   \n",
              "4           London to Valencia  March 2023              4.0            4.0   \n",
              "\n",
              "   food_quality  entertainment  wifi  ground_service  value_for_money  \\\n",
              "0           1.0            1.0   1.0             1.0                1   \n",
              "1           3.0            5.0   5.0             4.0                4   \n",
              "2           NaN            1.0   1.0             1.0                2   \n",
              "3           1.0            1.0   1.0             1.0                1   \n",
              "4           NaN            NaN   NaN             5.0                4   \n",
              "\n",
              "  recommended overall_rating  \\\n",
              "0          no              1   \n",
              "1         yes              9   \n",
              "2          no              2   \n",
              "3          no              1   \n",
              "4         yes             10   \n",
              "\n",
              "                                              review  \n",
              "0    My itinerary was supposed to be Las Vegas-Ch...  \n",
              "1    Busy day at LHR and flight full. Lounge crow...  \n",
              "2   Worst seats I have ever encountered in econom...  \n",
              "3   Top Ten REASONS to not use British Airways To...  \n",
              "4    Easy check in on the way to Heathrow. The fl...  "
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              "      <th></th>\n",
              "      <th>status</th>\n",
              "      <th>aircraft</th>\n",
              "      <th>travel_type</th>\n",
              "      <th>travel_class</th>\n",
              "      <th>route</th>\n",
              "      <th>date</th>\n",
              "      <th>seating_comfort</th>\n",
              "      <th>staff_service</th>\n",
              "      <th>food_quality</th>\n",
              "      <th>entertainment</th>\n",
              "      <th>wifi</th>\n",
              "      <th>ground_service</th>\n",
              "      <th>value_for_money</th>\n",
              "      <th>recommended</th>\n",
              "      <th>overall_rating</th>\n",
              "      <th>review</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Not Verified</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Solo Leisure</td>\n",
              "      <td>Economy Class</td>\n",
              "      <td>Las Vegas to Venice</td>\n",
              "      <td>May 2023</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>1</td>\n",
              "      <td>My itinerary was supposed to be Las Vegas-Ch...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>✅ Trip Verified</td>\n",
              "      <td>Boeing 777-300</td>\n",
              "      <td>Family Leisure</td>\n",
              "      <td>Premium Economy</td>\n",
              "      <td>London to New York</td>\n",
              "      <td>May 2023</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4</td>\n",
              "      <td>yes</td>\n",
              "      <td>9</td>\n",
              "      <td>Busy day at LHR and flight full. Lounge crow...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>✅ Trip Verified</td>\n",
              "      <td>A320</td>\n",
              "      <td>Couple Leisure</td>\n",
              "      <td>Economy Class</td>\n",
              "      <td>Lisbon to London</td>\n",
              "      <td>May 2023</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "      <td>2</td>\n",
              "      <td>Worst seats I have ever encountered in econom...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Not Verified</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Couple Leisure</td>\n",
              "      <td>Premium Economy</td>\n",
              "      <td>Dallas to Madrid via London</td>\n",
              "      <td>May 2023</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>1</td>\n",
              "      <td>Top Ten REASONS to not use British Airways To...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Not Verified</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Couple Leisure</td>\n",
              "      <td>Economy Class</td>\n",
              "      <td>London to Valencia</td>\n",
              "      <td>March 2023</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4</td>\n",
              "      <td>yes</td>\n",
              "      <td>10</td>\n",
              "      <td>Easy check in on the way to Heathrow. The fl...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-29b0e1d1-bb4f-4857-b95b-f69efaa0e0a0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-29b0e1d1-bb4f-4857-b95b-f69efaa0e0a0 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-29b0e1d1-bb4f-4857-b95b-f69efaa0e0a0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FJE3siGHgFLC",
        "outputId": "3b43ce10-28da-4d48-f1d1-f9bd3cf4d1df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3430 entries, 0 to 3429\n",
            "Data columns (total 16 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   status           2032 non-null   object \n",
            " 1   aircraft         1851 non-null   object \n",
            " 2   travel_type      2786 non-null   object \n",
            " 3   travel_class     3429 non-null   object \n",
            " 4   route            2781 non-null   object \n",
            " 5   date             2778 non-null   object \n",
            " 6   seating_comfort  3327 non-null   float64\n",
            " 7   staff_service    3317 non-null   float64\n",
            " 8   food_quality     3081 non-null   float64\n",
            " 9   entertainment    2365 non-null   float64\n",
            " 10  wifi             558 non-null    float64\n",
            " 11  ground_service   2716 non-null   float64\n",
            " 12  value_for_money  3430 non-null   int64  \n",
            " 13  recommended      3430 non-null   object \n",
            " 14  overall_rating   3426 non-null   object \n",
            " 15  review           3430 non-null   object \n",
            "dtypes: float64(6), int64(1), object(9)\n",
            "memory usage: 428.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "id": "1Jg8Gt7An3wP",
        "outputId": "2ccc62f4-fe33-4290-9f1c-4024d67d1087",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "status             1398\n",
              "aircraft           1579\n",
              "travel_type         644\n",
              "travel_class          1\n",
              "route               649\n",
              "date                652\n",
              "seating_comfort     103\n",
              "staff_service       113\n",
              "food_quality        349\n",
              "entertainment      1065\n",
              "wifi               2872\n",
              "ground_service      714\n",
              "value_for_money       0\n",
              "recommended           0\n",
              "overall_rating        4\n",
              "review                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 183
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.fillna(value=np.nan)"
      ],
      "metadata": {
        "id": "2jiYWhRWnknC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "aSeTZXNNgtOu",
        "outputId": "3d697f1c-8e72-4b92-c4b4-26e213a9567a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       seating_comfort  staff_service  food_quality  entertainment  \\\n",
              "count      3327.000000    3317.000000   3081.000000    2365.000000   \n",
              "mean          2.878870       3.261079      2.713405       2.653277   \n",
              "std           1.356947       1.484937      1.439915       1.407270   \n",
              "min           1.000000       1.000000      0.000000       0.000000   \n",
              "25%           2.000000       2.000000      1.000000       1.000000   \n",
              "50%           3.000000       3.000000      3.000000       3.000000   \n",
              "75%           4.000000       5.000000      4.000000       4.000000   \n",
              "max           5.000000       5.000000      5.000000       5.000000   \n",
              "\n",
              "             wifi  ground_service  value_for_money  \n",
              "count  558.000000     2716.000000      3430.000000  \n",
              "mean     1.935484        2.825479         2.702041  \n",
              "std      1.363593        1.449363         1.469778  \n",
              "min      1.000000        1.000000         1.000000  \n",
              "25%      1.000000        1.000000         1.000000  \n",
              "50%      1.000000        3.000000         3.000000  \n",
              "75%      3.000000        4.000000         4.000000  \n",
              "max      5.000000        5.000000         5.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-20f8879c-e11a-4c52-97dc-320df2bd0dc6\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>seating_comfort</th>\n",
              "      <th>staff_service</th>\n",
              "      <th>food_quality</th>\n",
              "      <th>entertainment</th>\n",
              "      <th>wifi</th>\n",
              "      <th>ground_service</th>\n",
              "      <th>value_for_money</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3327.000000</td>\n",
              "      <td>3317.000000</td>\n",
              "      <td>3081.000000</td>\n",
              "      <td>2365.000000</td>\n",
              "      <td>558.000000</td>\n",
              "      <td>2716.000000</td>\n",
              "      <td>3430.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.878870</td>\n",
              "      <td>3.261079</td>\n",
              "      <td>2.713405</td>\n",
              "      <td>2.653277</td>\n",
              "      <td>1.935484</td>\n",
              "      <td>2.825479</td>\n",
              "      <td>2.702041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.356947</td>\n",
              "      <td>1.484937</td>\n",
              "      <td>1.439915</td>\n",
              "      <td>1.407270</td>\n",
              "      <td>1.363593</td>\n",
              "      <td>1.449363</td>\n",
              "      <td>1.469778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "                                                     [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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations**\n",
        "- Average seating comfort is 2.88/5 \n",
        "- Staff service 3.26\n",
        "- Food quality 2.71\n",
        "- Entertainment "
      ],
      "metadata": {
        "id": "v2ZedHTThP_M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "vQ5tv03Kmf8e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting histograms\n",
        "df.hist(bins=10, edgecolor='black', figsize=(10, 6))\n",
        "\n",
        "# Adding titles and labels\n",
        "plt.suptitle('Histograms of British Airways Reviews', fontsize=14, fontweight='bold')\n",
        "plt.tight_layout(rect=[0, 0, 1, 0.95])  # Adjusting layout and spacing\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "id": "sjz-naIUmQCD",
        "outputId": "fb6f04e3-1c68-4659-8c28-35527c2a073c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3.9.13 ('venv': venv)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.13"
    },
    "orig_nbformat": 4,
    "vscode": {
      "interpreter": {
        "hash": "4f7924c4c56b083e0e50eadfe7ef592a7a8ef70df33a0047f82280e6be1afe15"
      }
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}