lab4_exploratory_data_analysis.ipynb

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    "colab": {
      "name": "lab4 exploratory data analysis.ipynb",
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      "collapsed_sections": [],
      "toc_visible": true,
      "machine_shape": "hm",
      "authorship_tag": "ABX9TyO/EvlZ/F9hC/T6btYxRfIU",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hkbu-kennycheng/comp3925/blob/main/lab4_exploratory_data_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# lab4 exploratory data analysis"
      ],
      "metadata": {
        "id": "PBYR7AgIuNxV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "import json\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from bs4 import BeautifulSoup\n",
        "from tqdm import tqdm"
      ],
      "metadata": {
        "id": "h0fHXi7CgCKZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "890f5f1c-0d0b-4d5e-a1c9-669562556e8c"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/requests/__init__.py:91: RequestsDependencyWarning: urllib3 (1.26.8) or chardet (3.0.4) doesn't match a supported version!\n",
            "  RequestsDependencyWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Revision of web data crawling"
      ],
      "metadata": {
        "id": "8dYyFjsa36Or"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ParkNShop"
      ],
      "metadata": {
        "id": "OCpsUo7jEhDr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Wrapping requests with a function which would return a `BeautifulSoup` object."
      ],
      "metadata": {
        "id": "WFtRbr9oqBdo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def searchParknShopSoup(keyword, perPage, page):\n",
        "  headers = {\n",
        "    'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/11.1.2 Safari/605.1.15'\n",
        "  }\n",
        "  r = requests.get(f'https://www.parknshop.com/search?q=&page={page}&resultsForPage={perPage}&text={keyword}&sort=&category2nd=0&category3rd=0&minSel=4.0&maxSel=1650.0&minSlider=4.0&maxSlider=1650.0&strictSearch=&_=1644309749305', headers=headers)\n",
        "  soup = BeautifulSoup(r.text)\n",
        "  return soup"
      ],
      "metadata": {
        "id": "SSDRgnbbwSOL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Make use of above function to develop a function for getting total number of products."
      ],
      "metadata": {
        "id": "uWAN1WKMqIVp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def searchParknShopTotal(keyword):\n",
        "  soup = searchParknShopSoup(keyword, 1, 1)\n",
        "  total = int(soup.select_one('.search-result-title-container .totalNumber').text)\n",
        "  return total "
      ],
      "metadata": {
        "id": "wdEvSi0yvSOh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "total = searchParknShopTotal('0')\n",
        "total"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WeRhutw7xDWQ",
        "outputId": "7584823b-43ec-47ac-ac23-e85b3c011235"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13945"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
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    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dVUMe6L_X_dh"
      },
      "outputs": [],
      "source": [
        "def searchParknShopWithPage(keyword, perPage, pageNum):\n",
        "  total = searchParknShopTotal(keyword)\n",
        "  if total == 0:\n",
        "    return pd.DataFrame(columns=['name', 'price'])\n",
        "    \n",
        "  soup = searchParknShopSoup(keyword, perPage, pageNum)\n",
        "  return pd.DataFrame([{\n",
        "      'name': ''.join([e.text.strip() for e in e.select('.name .text')]),\n",
        "      'price': ' '.join([e.text.strip().lstrip('HK$').replace(',','') for e in e.select('.discount')])\n",
        "  } for e in soup.select(\".product-container .item\")])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "perPage = 150\n",
        "df = searchParknShopWithPage('(0)', perPage, 1)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Ig7QZ6WqiJao",
        "outputId": "c96f7f80-de23-4cb4-dbd8-8edba088851b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-a21e4345-3a34-4451-87e5-f3198caacee6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Dishwash Detergent-lemon</td>\n",
              "      <td>21.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Oregano</td>\n",
              "      <td>20.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mr Juicy 100% Apple Juice</td>\n",
              "      <td>20.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Malkist Lemon Flavour Sandwich</td>\n",
              "      <td>19.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Scrub Sponge</td>\n",
              "      <td>9.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>85 Dark Chocolate</td>\n",
              "      <td>29.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>Oolong Tea Bags</td>\n",
              "      <td>10.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>Dried Apricots</td>\n",
              "      <td>29.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>Baked Beans In Tomato Sauce</td>\n",
              "      <td>9.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>Royal Castle White Rum</td>\n",
              "      <td>89.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a21e4345-3a34-4451-87e5-f3198caacee6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </button>\n",
              "      \n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "          document.querySelector('#df-a21e4345-3a34-4451-87e5-f3198caacee6 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-a21e4345-3a34-4451-87e5-f3198caacee6');\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",
              "  "
            ],
            "text/plain": [
              "                               name  price\n",
              "0          Dishwash Detergent-lemon  21.00\n",
              "1                           Oregano  20.50\n",
              "2         Mr Juicy 100% Apple Juice  20.50\n",
              "3    Malkist Lemon Flavour Sandwich  19.90\n",
              "4                      Scrub Sponge   9.90\n",
              "..                              ...    ...\n",
              "145               85 Dark Chocolate  29.90\n",
              "146                 Oolong Tea Bags  10.90\n",
              "147                  Dried Apricots  29.90\n",
              "148     Baked Beans In Tomato Sauce   9.90\n",
              "149          Royal Castle White Rum  89.00\n",
              "\n",
              "[150 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Looping through all pages to get all product data"
      ],
      "metadata": {
        "id": "09K9CJjzqP6T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i in tqdm(range(1, total // perPage + 1)):\n",
        "  df = df.append(searchParknShopWithPage('0', perPage, i))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Imtq256SfsbZ",
        "outputId": "1f3257ba-afef-413b-ec0d-b0b0b4f831ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 92/92 [22:44<00:00, 14.83s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Save as a csv file for backup"
      ],
      "metadata": {
        "id": "0yONGepHqU75"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('pns.csv')"
      ],
      "metadata": {
        "id": "KcZOE6d_gAqw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://gist.githubusercontent.com/hkbu-kennycheng/2f08dff0e3c7886c5e607556b2fed21d/raw/f1f3a6794e6deb9b75ec3084a11b21ac247d822d/pns.csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o7GgRpqBzI1-",
        "outputId": "02fabed5-8448-43a7-ac38-4f5b441cc43a"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-02-09 03:58:01--  https://gist.githubusercontent.com/hkbu-kennycheng/2f08dff0e3c7886c5e607556b2fed21d/raw/f1f3a6794e6deb9b75ec3084a11b21ac247d822d/pns.csv\n",
            "Resolving gist.githubusercontent.com (gist.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.111.133, ...\n",
            "Connecting to gist.githubusercontent.com (gist.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 518112 (506K) [text/plain]\n",
            "Saving to: ‘pns.csv’\n",
            "\n",
            "pns.csv             100%[===================>] 505.97K  --.-KB/s    in 0.05s   \n",
            "\n",
            "2022-02-09 03:58:02 (10.9 MB/s) - ‘pns.csv’ saved [518112/518112]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('pns.csv', index_col=0)"
      ],
      "metadata": {
        "id": "bUx2b_i4j5oT"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "PjJI6qBXsYqO",
        "outputId": "fc7b584f-c5e2-4665-beda-313c98a56905"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e7869304-0dcb-47c6-b1aa-6f68e5d2c50a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Ham Sliced</td>\n",
              "      <td>39.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Instant Oatmeal</td>\n",
              "      <td>27.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Dishwash Detergent-lemon</td>\n",
              "      <td>21.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Oregano</td>\n",
              "      <td>20.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Mr Juicy 100% Apple Juice</td>\n",
              "      <td>20.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13749</th>\n",
              "      <td>Finland Pork Tenderloin</td>\n",
              "      <td>50.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13750</th>\n",
              "      <td>Asw Lime + Sailormoon Plate (random Pick)</td>\n",
              "      <td>43.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13751</th>\n",
              "      <td>Frozen Handcraft Udon</td>\n",
              "      <td>69.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13752</th>\n",
              "      <td>Foaming Hw - Bottle + Refill (baby Powder)</td>\n",
              "      <td>49.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13753</th>\n",
              "      <td>Okinawa P Sweet Potato Soyabean Milk</td>\n",
              "      <td>19.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13754 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e7869304-0dcb-47c6-b1aa-6f68e5d2c50a')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e7869304-0dcb-47c6-b1aa-6f68e5d2c50a 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-e7869304-0dcb-47c6-b1aa-6f68e5d2c50a');\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",
              "  "
            ],
            "text/plain": [
              "                                             name  price\n",
              "0                                      Ham Sliced   39.9\n",
              "1                                 Instant Oatmeal   27.9\n",
              "2                        Dishwash Detergent-lemon   21.0\n",
              "3                                         Oregano   20.5\n",
              "4                       Mr Juicy 100% Apple Juice   20.5\n",
              "...                                           ...    ...\n",
              "13749                     Finland Pork Tenderloin   50.9\n",
              "13750   Asw Lime + Sailormoon Plate (random Pick)   43.9\n",
              "13751                       Frozen Handcraft Udon   69.9\n",
              "13752  Foaming Hw - Bottle + Refill (baby Powder)   49.9\n",
              "13753        Okinawa P Sweet Potato Soyabean Milk   19.9\n",
              "\n",
              "[13754 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "33-y9nVW3K1D",
        "outputId": "98750fcc-50d1-4ed4-8521-550238681e9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-4ce5bcd8-24bb-4cf9-ba0e-abbc327c4e9f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>13751.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>63.211244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>108.908153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>19.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>34.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>67.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2245.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ce5bcd8-24bb-4cf9-ba0e-abbc327c4e9f')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4ce5bcd8-24bb-4cf9-ba0e-abbc327c4e9f 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-4ce5bcd8-24bb-4cf9-ba0e-abbc327c4e9f');\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",
              "  "
            ],
            "text/plain": [
              "              price\n",
              "count  13751.000000\n",
              "mean      63.211244\n",
              "std      108.908153\n",
              "min        2.800000\n",
              "25%       19.900000\n",
              "50%       34.900000\n",
              "75%       67.000000\n",
              "max     2245.000000"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['price'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Lmzy5YZ3N6S",
        "outputId": "8b6cadcf-1efb-4a75-f0ab-8595d165698c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    13751.000000\n",
              "mean        63.211244\n",
              "std        108.908153\n",
              "min          2.800000\n",
              "25%         19.900000\n",
              "50%         34.900000\n",
              "75%         67.000000\n",
              "max       2245.000000\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Replace with `np.nan`"
      ],
      "metadata": {
        "id": "ZqBGL7TbRx1Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['price'].replace('', np.nan, inplace=True)"
      ],
      "metadata": {
        "id": "Ij_LVjVvRzoO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Mannings HK"
      ],
      "metadata": {
        "id": "wqH7vD2KikpY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def tryMannings(keyword, pageNum):\n",
        "  headers = {\n",
        "    'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/11.1.2 Safari/605.1.15'\n",
        "  }\n",
        "  r = requests.get(f'https://www.mannings.com.hk/search?q={keyword}&page={pageNum}', headers=headers)\n",
        "  return r.text"
      ],
      "metadata": {
        "id": "Ez489joqin46"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tryMannings('2',0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Dty2DMWfizZ9",
        "outputId": "cd475bd3-0eac-425f-f8ce-25c640f77e9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
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href=\"//assets.queue-it.net/static/QueueFront/img/queue-it_appletouch_7ca7792e6a9ca7868f574af6b9a7804d.png\" />\\n<link rel=\"icon\" type=\"image/png\" href=\"//assets.queue-it.net/static/QueueFront/img/favicon-32x32_b9bbae2ffa6bd8c9f22cfdfc6d77142a.png\" sizes=\"32x32\" />\\n<link rel=\"icon\" type=\"image/png\" href=\"//assets.queue-it.net/static/QueueFront/img/favicon-16x16_1c313213750977118fc9bccb99b43c3e.png\" sizes=\"16x16\" />\\n<link rel=\"shortcut icon\" href=\"//assets.queue-it.net/static/QueueFront/img/queue-it_f0e5daa9ac1a2225a6bad64734da1ebd.ico\" />\\n<meta name=\"application-name\" content=\"&nbsp;\" />\\n<meta name=\"msapplication-TileColor\" content=\"#FFFFFF\" />\\n<meta name=\"msapplication-TileImage\" content=\"//assets.queue-it.net/static/QueueFront/img/mstile-144x144_7ca7792e6a9ca7868f574af6b9a7804d.png\" />\\n    <link rel=\\'stylesheet\\' href=\\'//assets.queue-it.net/static/QueueFront/css/combined/style_c102db4f8b29ceabb83ea58e5d469286.css\\' type=\\'text/css\\' /><!--[if lte IE 7]><link rel=\\'stylesheet\\' href=\\'//assets.queue-it.net/static/QueueFront/css/combined/ie_9b8e0476ed11438548f82f973a454da3.css\\' type=\\'text/css\\' /><![endif]--><link rel=\\'stylesheet\\' href=\\'//assets.queue-it.net/dairyfarmgroup/userdata/v5/style01.css\\' type=\\'text/css\\' />\\n\\n    <script>\\n    (function () {\\n        if (!window.console)\\n            window.console = {};\\n\\n        if (!window.console.log)\\n            window.console.log = function () { };\\n\\n        if (!window.console.debug)\\n            window.console.debug = function () { };\\n    })();\\n</script>\\n<script>\\n    function getScriptBasePath() {\\n        return \\'/Script/combined/\\';\\n    }\\n</script>\\n<script type=\"text/javascript\" src=\"//assets.queue-it.net/static/QueueFront/Script/combined/common_a387adf3f9b4d19118846d173b343b00.js\" integrity=\"sha384-hG/wNs+x8L4wLW0QNSK+L5CORd6oKxNr0Mu55wfCjuSRK6wF2UoxHnleayfMX6j2\" 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proofOfWorkHost: \\'dairyfarmgroup.queue-it.net\\',\\n        botDetectSource: \\'//assets.queue-it.net/static/challenge/script/botdetect.min.js\\',\\n        botDetectHost: \\'dairyfarmgroup.queue-it.net\\',\\n        captchaPublicKey: \\'6Lc9sScUAAAAALTk003eM2ytnYGGKQaQa7usPKwo\\',\\n        captchaInvisiblePublicKey: \\'6LePTyoUAAAAADPttQg1At44EFCygqxZYzgleaKp\\',\\n        challenges: [{\"name\":\"Recaptcha\",\"hasAnimation\":false}],\\n        culture: \\'zh-HK\\'\\n    });\\n\\n    $(document).ready(function () {\\n        window.queueViewModel.onReady(\"softblock\");\\n    });\\n</script>\\n\\n    <script type=\\'text/javascript\\' src=\\'//assets.queue-it.net/dairyfarmgroup/userdata/v5/script04.js\\'></script>\\n\\n</head>\\n<body data-pageid=\"softblock\" data-culture=\"zh-HK\" class=\"softblock\">\\n    <div id=\"toppanel\" data-bind=\"visible: layout.topPanelVisible\">\\n    <iframe id=\\'toppanel_iframe\\' src=\\'//assets.queue-it.net/dairyfarmgroup/userdata/v5/top01.html?c=dairyfarmgroup&e=manning&q=00000000-0000-0000-0000-000000000000&cid=zh-HK\\' allowtransparency=\\'true\\' frameborder=\\'0\\' scrolling=\\'no\\' data-bind=\"iframe-src-attr: { src: texts.toppanelIFrameSrc }\" title=\"top panel content\"></iframe>\\n</div>\\n\\n    <div id=\"wrapper\">\\n        <div id=\"leftpanel\" style=\"display: none;\" data-bind=\"visible: layout.leftPanelVisible\">\\n    <iframe id=\\'leftpanel_iframe\\' src=\\'\\' allowtransparency=\\'true\\' frameborder=\\'0\\' scrolling=\\'no\\' data-bind=\"iframe-src-attr: { src: texts.leftpanelIFrameSrc }\" title=\"right panel content\"></iframe>\\n</div>\\n\\n        <div id=\"main\">\\n            <div id=\"main-top\">\\n    <div id=\"language-selector\" title=\"select display language\" style=\"display: none;\" data-bind=\"visible: languageSelector.languages().length > 1 && languageSelector.languageSelectorVisible\">\\n        <select id=\"SharedTexts_Languages_SelectedValue\" data-bind=\"options: languageSelector.languages, optionsText:\\'text\\', optionsValue:\\'value\\', value:languageSelector.selectedLanguage \"></select>\\n    </div>\\n    <div id=\"main-top-message\">\\n        <p class=\"item\">\\n            <span id=\"lbCookieInfo\" data-bind=\"text: texts.cookiesInfoText, attr: { class: texts.cookiesInfoClass, title: texts.cookiesInfoTooltip }\"></span>\\n        </p>\\n    </div>\\n</div>\\n\\n            <div id=\"main_t\" class=\"t\"></div>\\n            <div id=\"main_c\" class=\"c\">\\n                <div id=\"content\">\\n                    <div id=\"header\">\\n    <h1 class=\"logo\" style=\"display: none;\" data-bind=\"visible: layout.logoVisible\">\\n        <img src=\"//assets.queue-it.net/static/QueueFront/img/queue-it_logo_c20bdd104f98eb49499434163ebdb42b.png\" id=\"imgCustomerLogo\" alt=\"logo\" longdesc=\"company logo for Master Concept (Hong Kong) Limited\" data-bind=\"attr: { src: texts.logoSrc }\" />\\n    </h1>\\n    <div id=\"headerparagraph\">\\n        <span id=\"lbHeaderP\" data-bind=\"html: texts.body\">你正在等候進入萬寧網上商店購物 ，請耐心等候。\\n請勿關閉此頁，你即將進入網頁</span>\\n    </div>\\n</div>\\n\\n                    \\n<div id=\"MainPart_divWarningBox\">\\n    <div id=\"divChallenge\">\\n        <div id=\"divChallenge_Content\">\\n            <div id=\"challenge-widget-container\">\\n                <div id=\"challenge-container\"></div>\\n                <div class=\"hidden\" id=\"three-bar-loader-container\">\\n                    <div class=\"three-bar-loader\"></div>\\n                    <div class=\"three-bar-loader\"></div>\\n                    <div class=\"three-bar-loader\"></div>\\n                </div>\\n            </div>\\n        </div>\\n    </div>\\n</div>\\n                </div>\\n                <div id=\"middlepanel\" style=\"display: none;\" data-bind=\"visible: layout.middlePanelVisible\">\\n    <iframe id=\\'middlepanel_iframe\\' src=\\'\\' allowtransparency=\\'true\\' frameborder=\\'0\\' scrolling=\\'no\\' data-bind=\"iframe-src-attr: { src: texts.middlepanelIFrameSrc }\" title=\"middle panel content\"></iframe>\\n</div>\\n\\n                    <div id=\"footer\">\\n        <div id=\"footer-disclaimer\" data-bind=\"html: texts.disclaimerText\"></div>\\n\\n        <div id=\"footer-direct-link\" style=\"display: none\" data-bind=\"visible: hasQueueId\"><span id=\"hlLinkToQueueTicket2Text\">&#x968A;&#x5217;ID&#xFF1A;</span> <a id=\"hlLinkToQueueTicket2\" data-bind=\"text: queueId, click: toggleQueueIdLinkModal\">00000000-0000-0000-0000-000000000000</a></div>       \\n            <div id=\"footer-queueit-link\">\\n                <strong class=\"by\">\\n                    <a href=\"https://queue-it.com/what-is-this.html?customerId=dairyfarmgroup&amp;eventId=manning&amp;queueId=00000000-0000-0000-0000-000000000000&amp;language=zh-HK\" id=\"aThisIsQueueIt\" target=\"_blank\">QUEUE-IT</a>\\n                </strong>\\n            </div>\\n    </div>\\n            </div>\\n            <div id=\"MainPart_divVersionInfo\" class=\"b\" title=\"4.4.447.0\"></div>\\n        </div>\\n        <div id=\"rightpanel\" style=\"display: none;\" data-bind=\"visible: layout.rightPanelVisible\">\\n    <iframe id=\\'rightpanel_iframe\\' src=\\'\\' allowtransparency=\\'true\\' frameborder=\\'0\\' scrolling=\\'no\\' data-bind=\"iframe-src-attr: { src: texts.rightpanelIFrameSrc }\" title=\"right panel content\"></iframe>\\n</div>\\n\\n    </div>\\n    <div id=\"sidebar\" style=\"display: none;\" data-bind=\"visible: layout.sidePanelVisible\">\\n    <iframe id=\\'sidebar_iframe\\' src=\\'\\' allowtransparency=\\'true\\' frameborder=\\'0\\' scrolling=\\'no\\' data-bind=\"iframe-src-attr: { src: texts.sidepanelIFrameSrc }\" title=\"sidebar content\"></iframe>\\n</div>\\n\\n    <div id=\"bottompanel\" style=\"display: none;\" data-bind=\"visible: layout.bottomPanelVisible\">\\n    <iframe id=\\'bottompanel_iframe\\' src=\\'\\' allowtransparency=\\'true\\' frameborder=\\'0\\' scrolling=\\'no\\' data-bind=\"iframe-src-attr: { src: texts.bottompanelIFrameSrc }\" title=\"bottom panel content\"></iframe>\\n</div>\\n\\n\\n\\n</body>\\n</html>\\n'"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Advanced web crawling with pyppeteer"
      ],
      "metadata": {
        "id": "W1faZof5AFob"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!apt update\n",
        "!apt install chromium-browser chromium-chromedriver fonts-noto fonts-noto-cjk"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5um7KvCAAFML",
        "outputId": "bda8cf5b-7e55-4c79-9d6f-6da707adb47a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Get:1 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease [3,626 B]\n",
            "Ign:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease\n",
            "Ign:3 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  InRelease\n",
            "Get:4 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release [696 B]\n",
            "Hit:5 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release\n",
            "Get:6 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic InRelease [15.9 kB]\n",
            "Hit:7 http://archive.ubuntu.com/ubuntu bionic InRelease\n",
            "Get:8 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release.gpg [836 B]\n",
            "Get:9 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB]\n",
            "Get:10 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB]\n",
            "Hit:12 http://ppa.launchpad.net/cran/libgit2/ubuntu bionic InRelease\n",
            "Get:13 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Packages [917 kB]\n",
            "Get:14 http://archive.ubuntu.com/ubuntu bionic-backports InRelease [74.6 kB]\n",
            "Hit:15 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic InRelease\n",
            "Get:16 http://security.ubuntu.com/ubuntu bionic-security/multiverse amd64 Packages [21.1 kB]\n",
            "Get:17 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease [21.3 kB]\n",
            "Get:18 http://security.ubuntu.com/ubuntu bionic-security/restricted amd64 Packages [781 kB]\n",
            "Get:19 http://archive.ubuntu.com/ubuntu bionic-updates/restricted amd64 Packages [815 kB]\n",
            "Get:20 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main Sources [1,825 kB]\n",
            "Get:21 http://security.ubuntu.com/ubuntu bionic-security/universe amd64 Packages [1,466 kB]\n",
            "Get:22 http://archive.ubuntu.com/ubuntu bionic-updates/multiverse amd64 Packages [29.1 kB]\n",
            "Get:23 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 Packages [2,995 kB]\n",
            "Get:24 http://security.ubuntu.com/ubuntu bionic-security/main amd64 Packages [2,557 kB]\n",
            "Get:25 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 Packages [2,244 kB]\n",
            "Get:26 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main amd64 Packages [935 kB]\n",
            "Get:27 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic/main amd64 Packages [42.8 kB]\n",
            "Fetched 14.9 MB in 8s (1,933 kB/s)\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "62 packages can be upgraded. Run 'apt list --upgradable' to see them.\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following packages were automatically installed and are no longer required:\n",
            "  cuda-command-line-tools-10-0 cuda-command-line-tools-10-1\n",
            "  cuda-command-line-tools-11-0 cuda-compiler-10-0 cuda-compiler-10-1\n",
            "  cuda-compiler-11-0 cuda-cuobjdump-10-0 cuda-cuobjdump-10-1\n",
            "  cuda-cuobjdump-11-0 cuda-cupti-10-0 cuda-cupti-10-1 cuda-cupti-11-0\n",
            "  cuda-cupti-dev-11-0 cuda-documentation-10-0 cuda-documentation-10-1\n",
            "  cuda-documentation-11-0 cuda-documentation-11-1 cuda-gdb-10-0 cuda-gdb-10-1\n",
            "  cuda-gdb-11-0 cuda-gpu-library-advisor-10-0 cuda-gpu-library-advisor-10-1\n",
            "  cuda-libraries-10-0 cuda-libraries-10-1 cuda-libraries-11-0\n",
            "  cuda-memcheck-10-0 cuda-memcheck-10-1 cuda-memcheck-11-0 cuda-nsight-10-0\n",
            "  cuda-nsight-10-1 cuda-nsight-11-0 cuda-nsight-11-1 cuda-nsight-compute-10-0\n",
            "  cuda-nsight-compute-10-1 cuda-nsight-compute-11-0 cuda-nsight-compute-11-1\n",
            "  cuda-nsight-systems-10-1 cuda-nsight-systems-11-0 cuda-nsight-systems-11-1\n",
            "  cuda-nvcc-10-0 cuda-nvcc-10-1 cuda-nvcc-11-0 cuda-nvdisasm-10-0\n",
            "  cuda-nvdisasm-10-1 cuda-nvdisasm-11-0 cuda-nvml-dev-10-0 cuda-nvml-dev-10-1\n",
            "  cuda-nvml-dev-11-0 cuda-nvprof-10-0 cuda-nvprof-10-1 cuda-nvprof-11-0\n",
            "  cuda-nvprune-10-0 cuda-nvprune-10-1 cuda-nvprune-11-0 cuda-nvtx-10-0\n",
            "  cuda-nvtx-10-1 cuda-nvtx-11-0 cuda-nvvp-10-0 cuda-nvvp-10-1 cuda-nvvp-11-0\n",
            "  cuda-nvvp-11-1 cuda-samples-10-0 cuda-samples-10-1 cuda-samples-11-0\n",
            "  cuda-samples-11-1 cuda-sanitizer-11-0 cuda-sanitizer-api-10-1\n",
            "  cuda-toolkit-10-0 cuda-toolkit-10-1 cuda-toolkit-11-0 cuda-toolkit-11-1\n",
            "  cuda-tools-10-0 cuda-tools-10-1 cuda-tools-11-0 cuda-tools-11-1\n",
            "  cuda-visual-tools-10-0 cuda-visual-tools-10-1 cuda-visual-tools-11-0\n",
            "  cuda-visual-tools-11-1 default-jre dkms freeglut3 freeglut3-dev\n",
            "  keyboard-configuration libargon2-0 libcap2 libcryptsetup12\n",
            "  libdevmapper1.02.1 libfontenc1 libidn11 libip4tc0 libjansson4\n",
            "  libnvidia-cfg1-510 libnvidia-common-460 libnvidia-common-510\n",
            "  libnvidia-extra-510 libnvidia-fbc1-510 libnvidia-gl-510 libpam-systemd\n",
            "  libpolkit-agent-1-0 libpolkit-backend-1-0 libpolkit-gobject-1-0 libxfont2\n",
            "  libxi-dev libxkbfile1 libxmu-dev libxmu-headers libxnvctrl0\n",
            "  nsight-compute-2020.2.1 nsight-compute-2022.1.0 nsight-systems-2020.3.2\n",
            "  nsight-systems-2020.3.4 nsight-systems-2021.5.2 nvidia-dkms-510\n",
            "  nvidia-kernel-common-510 nvidia-kernel-source-510 nvidia-modprobe\n",
            "  nvidia-settings openjdk-11-jre policykit-1 policykit-1-gnome python3-xkit\n",
            "  screen-resolution-extra systemd systemd-sysv udev x11-xkb-utils\n",
            "  xserver-common xserver-xorg-core-hwe-18.04 xserver-xorg-video-nvidia-510\n",
            "Use 'apt autoremove' to remove them.\n",
            "The following additional packages will be installed:\n",
            "  chromium-browser-l10n chromium-codecs-ffmpeg-extra fonts-noto-hinted\n",
            "  fonts-noto-mono fonts-noto-unhinted\n",
            "Suggested packages:\n",
            "  webaccounts-chromium-extension unity-chromium-extension fonts-noto-cjk-extra\n",
            "The following NEW packages will be installed:\n",
            "  chromium-browser chromium-browser-l10n chromium-chromedriver\n",
            "  chromium-codecs-ffmpeg-extra fonts-noto fonts-noto-cjk fonts-noto-hinted\n",
            "  fonts-noto-mono fonts-noto-unhinted\n",
            "0 upgraded, 9 newly installed, 0 to remove and 62 not upgraded.\n",
            "Need to get 159 MB of archives.\n",
            "After this operation, 450 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 chromium-codecs-ffmpeg-extra amd64 97.0.4692.71-0ubuntu0.18.04.1 [1,142 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 chromium-browser amd64 97.0.4692.71-0ubuntu0.18.04.1 [84.7 MB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 chromium-browser-l10n all 97.0.4692.71-0ubuntu0.18.04.1 [4,370 kB]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 chromium-chromedriver amd64 97.0.4692.71-0ubuntu0.18.04.1 [5,055 kB]\n",
            "Get:5 http://archive.ubuntu.com/ubuntu bionic/universe amd64 fonts-noto-hinted all 20171026-2 [6,653 kB]\n",
            "Get:6 http://archive.ubuntu.com/ubuntu bionic/universe amd64 fonts-noto all 20171026-2 [4,136 B]\n",
            "Get:7 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 fonts-noto-cjk all 1:20190409+repack1-0ubuntu0.18.04 [55.7 MB]\n",
            "Get:8 http://archive.ubuntu.com/ubuntu bionic/main amd64 fonts-noto-mono all 20171026-2 [75.5 kB]\n",
            "Get:9 http://archive.ubuntu.com/ubuntu bionic/universe amd64 fonts-noto-unhinted all 20171026-2 [1,576 kB]\n",
            "Fetched 159 MB in 17s (9,639 kB/s)\n",
            "Selecting previously unselected package chromium-codecs-ffmpeg-extra.\n",
            "(Reading database ... 155113 files and directories currently installed.)\n",
            "Preparing to unpack .../0-chromium-codecs-ffmpeg-extra_97.0.4692.71-0ubuntu0.18.04.1_amd64.deb ...\n",
            "Unpacking chromium-codecs-ffmpeg-extra (97.0.4692.71-0ubuntu0.18.04.1) ...\n",
            "Selecting previously unselected package chromium-browser.\n",
            "Preparing to unpack .../1-chromium-browser_97.0.4692.71-0ubuntu0.18.04.1_amd64.deb ...\n",
            "Unpacking chromium-browser (97.0.4692.71-0ubuntu0.18.04.1) ...\n",
            "Selecting previously unselected package chromium-browser-l10n.\n",
            "Preparing to unpack .../2-chromium-browser-l10n_97.0.4692.71-0ubuntu0.18.04.1_all.deb ...\n",
            "Unpacking chromium-browser-l10n (97.0.4692.71-0ubuntu0.18.04.1) ...\n",
            "Selecting previously unselected package chromium-chromedriver.\n",
            "Preparing to unpack .../3-chromium-chromedriver_97.0.4692.71-0ubuntu0.18.04.1_amd64.deb ...\n",
            "Unpacking chromium-chromedriver (97.0.4692.71-0ubuntu0.18.04.1) ...\n",
            "Selecting previously unselected package fonts-noto-hinted.\n",
            "Preparing to unpack .../4-fonts-noto-hinted_20171026-2_all.deb ...\n",
            "Unpacking fonts-noto-hinted (20171026-2) ...\n",
            "Selecting previously unselected package fonts-noto.\n",
            "Preparing to unpack .../5-fonts-noto_20171026-2_all.deb ...\n",
            "Unpacking fonts-noto (20171026-2) ...\n",
            "Selecting previously unselected package fonts-noto-cjk.\n",
            "Preparing to unpack .../6-fonts-noto-cjk_1%3a20190409+repack1-0ubuntu0.18.04_all.deb ...\n",
            "Unpacking fonts-noto-cjk (1:20190409+repack1-0ubuntu0.18.04) ...\n",
            "Selecting previously unselected package fonts-noto-mono.\n",
            "Preparing to unpack .../7-fonts-noto-mono_20171026-2_all.deb ...\n",
            "Unpacking fonts-noto-mono (20171026-2) ...\n",
            "Selecting previously unselected package fonts-noto-unhinted.\n",
            "Preparing to unpack .../8-fonts-noto-unhinted_20171026-2_all.deb ...\n",
            "Unpacking fonts-noto-unhinted (20171026-2) ...\n",
            "Setting up fonts-noto-unhinted (20171026-2) ...\n",
            "Setting up fonts-noto-cjk (1:20190409+repack1-0ubuntu0.18.04) ...\n",
            "Setting up fonts-noto-mono (20171026-2) ...\n",
            "Setting up fonts-noto-hinted (20171026-2) ...\n",
            "Setting up chromium-codecs-ffmpeg-extra (97.0.4692.71-0ubuntu0.18.04.1) ...\n",
            "Setting up fonts-noto (20171026-2) ...\n",
            "Setting up chromium-browser (97.0.4692.71-0ubuntu0.18.04.1) ...\n",
            "update-alternatives: using /usr/bin/chromium-browser to provide /usr/bin/x-www-browser (x-www-browser) in auto mode\n",
            "update-alternatives: using /usr/bin/chromium-browser to provide /usr/bin/gnome-www-browser (gnome-www-browser) in auto mode\n",
            "Setting up chromium-chromedriver (97.0.4692.71-0ubuntu0.18.04.1) ...\n",
            "Setting up chromium-browser-l10n (97.0.4692.71-0ubuntu0.18.04.1) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "Processing triggers for hicolor-icon-theme (0.17-2) ...\n",
            "Processing triggers for fontconfig (2.12.6-0ubuntu2) ...\n",
            "Processing triggers for mime-support (3.60ubuntu1) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1.3) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pyppeteer nest_asyncio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        },
        "id": "2U0vGOcLCVub",
        "outputId": "acddb7df-16fa-4d41-a5f9-fbeb69718eaf"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pyppeteer\n",
            "  Downloading pyppeteer-1.0.2-py3-none-any.whl (83 kB)\n",
            "\u001b[?25l\r\u001b[K     |████                            | 10 kB 25.0 MB/s eta 0:00:01\r\u001b[K     |███████▉                        | 20 kB 7.9 MB/s eta 0:00:01\r\u001b[K     |███████████▉                    | 30 kB 7.7 MB/s eta 0:00:01\r\u001b[K     |███████████████▊                | 40 kB 7.2 MB/s eta 0:00:01\r\u001b[K     |███████████████████▋            | 51 kB 4.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▋        | 61 kB 4.5 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▌    | 71 kB 4.5 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▍| 81 kB 5.1 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 83 kB 1.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: nest_asyncio in /usr/local/lib/python3.7/dist-packages (1.5.4)\n",
            "Requirement already satisfied: appdirs<2.0.0,>=1.4.3 in /usr/local/lib/python3.7/dist-packages (from pyppeteer) (1.4.4)\n",
            "Collecting urllib3<2.0.0,>=1.25.8\n",
            "  Downloading urllib3-1.26.8-py2.py3-none-any.whl (138 kB)\n",
            "\u001b[K     |████████████████████████████████| 138 kB 25.6 MB/s \n",
            "\u001b[?25hCollecting websockets<11.0,>=10.0\n",
            "  Downloading websockets-10.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (111 kB)\n",
            "\u001b[K     |████████████████████████████████| 111 kB 55.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm<5.0.0,>=4.42.1 in /usr/local/lib/python3.7/dist-packages (from pyppeteer) (4.62.3)\n",
            "Collecting pyee<9.0.0,>=8.1.0\n",
            "  Downloading pyee-8.2.2-py2.py3-none-any.whl (12 kB)\n",
            "Requirement already satisfied: certifi>=2021 in /usr/local/lib/python3.7/dist-packages (from pyppeteer) (2021.10.8)\n",
            "Requirement already satisfied: importlib-metadata>=1.4 in /usr/local/lib/python3.7/dist-packages (from pyppeteer) (4.10.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=1.4->pyppeteer) (3.10.0.2)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=1.4->pyppeteer) (3.7.0)\n",
            "Installing collected packages: websockets, urllib3, pyee, pyppeteer\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "requests 2.23.0 requires urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1, but you have urllib3 1.26.8 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed pyee-8.2.2 pyppeteer-1.0.2 urllib3-1.26.8 websockets-10.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "urllib3"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import asyncio\n",
        "import nest_asyncio\n",
        "from pyppeteer import launch\n",
        "\n",
        "nest_asyncio.apply()"
      ],
      "metadata": {
        "id": "UxZdtLNqEkqL"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Saving screenshot"
      ],
      "metadata": {
        "id": "szcvAoCYdHSG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "async def main():\n",
        "  browser = await launch({'executablePath':'/usr/bin/chromium-browser', 'headless': True, 'args':['--no-sandbox']})\n",
        "  page = await browser.newPage() # open a new browser window\n",
        "  await page.goto('https://www.mannings.com.hk/search?q=2&page=27') # goto the url\n",
        "  await page.screenshot({'path': 'example.png'}) # save screenshot as example.png\n",
        "  await browser.close() # close browser window\n",
        "\n",
        "asyncio.get_event_loop().run_until_complete(main())"
      ],
      "metadata": {
        "id": "tVcDHewidJ-g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab.patches import cv2_imshow\n",
        "import cv2\n",
        "\n",
        "img = cv2.imread('example.png')\n",
        "cv2_imshow(img)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "vspF3g9jeqRO",
        "outputId": "11d2b443-3f54-46ee-cb75-64d3cba46a89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=800x600 at 0x7FAB9FAE4DD0>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Getting text in a single element"
      ],
      "metadata": {
        "id": "8VPZ36A4c4EQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "async def searchTotal():\n",
        "  browser = await launch({'executablePath':'/usr/bin/chromium-browser', 'headless': True, 'args':['--no-sandbox']})\n",
        "  page = await browser.newPage()\n",
        "  await page.goto('https://www.mannings.com.hk/search?q=2&page=0', { 'waitUntil':'networkidle0' })\n",
        "  await page.screenshot({'path': 'error.png'})\n",
        "  total = await page.Jeval('.pagination-bar-results b', 'function(node) { return node.textContent }')\n",
        "  print(total.strip())\n",
        "  await browser.close()"
      ],
      "metadata": {
        "id": "ySu2ZEohW-u6"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To run `async` function, we need to pass the function into `asyncio.get_event_loop().run_until_complete`."
      ],
      "metadata": {
        "id": "QK7L5TBr_Kjr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "asyncio.get_event_loop().run_until_complete(searchTotal())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EBYH8Uhy_Jem",
        "outputId": "622ed190-06f7-4aac-a904-562448010d1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5,629 Product results found\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Wrapping up long code statement via function with simple name."
      ],
      "metadata": {
        "id": "kqfjyq3of8FM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def run(func):\n",
        "  return asyncio.get_event_loop().run_until_complete(func)"
      ],
      "metadata": {
        "id": "-Vp1T65pfuPt"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "run(searchTotal())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qw2B2jPfgFKI",
        "outputId": "57d9b087-d48c-43ca-96bc-1775ebd266b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5,629 Product results found\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Getting return value from `async` function with `asyncio.gather`. `asyncio.gather` allows multiple functions as input, thus it will return a list of return values of each function. "
      ],
      "metadata": {
        "id": "LgR5HVSpfBIQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def runAndReturn(func):\n",
        "  return asyncio.get_event_loop().run_until_complete(asyncio.gather(func))[0]"
      ],
      "metadata": {
        "id": "Sf63tA1Vgl2F"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "async def searchManningsTotal(keyword):\n",
        "  browser = await launch({'executablePath':'/usr/bin/chromium-browser', 'headless': True, 'args':['--no-sandbox']})\n",
        "  page = await browser.newPage()\n",
        "  await page.goto(f'https://www.mannings.com.hk/search?q={keyword}', { 'waitUntil':'networkidle0' })\n",
        "  total = int((await page.Jeval('.pagination-bar-results b', 'function(node) { return node.textContent }')).replace('Product results found','').replace(',',''))\n",
        "  await browser.close()\n",
        "  return total"
      ],
      "metadata": {
        "id": "0eNHolT1b1Of"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "total = runAndReturn(searchManningsTotal('2'))\n",
        "print(total)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6NwXUONY_GnW",
        "outputId": "a84b0887-a4ea-4e3f-b8f6-6a53b146e87c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5629\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Looping throught multiple elements with `page.JJ`."
      ],
      "metadata": {
        "id": "PwcV7OQDhgjS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "async def searchManningsWithPage(keyword, page):\n",
        "  browser = await launch({'executablePath':'/usr/bin/chromium-browser', 'headless': True, 'args':['--no-sandbox']})\n",
        "  context = await browser.createIncognitoBrowserContext() # create an incognito window\n",
        "  page = await context.newPage() # open a new tab in browser\n",
        "  await page.goto(f'https://www.mannings.com.hk/search?q={keyword}&page={page}', { 'waitUntil':'networkidle0' })\n",
        "  data = [{\n",
        "    'name': (await e.Jeval('.product_name','function(node) { return node.textContent }')).strip(),\n",
        "    'price': float((await e.Jeval('.price','function(node) { return node.textContent }')).strip().lstrip('$').replace(',',''))\n",
        "  } for e in await page.JJ('.product_content')]\n",
        "  await context.close()\n",
        "  await browser.close()\n",
        "  return pd.DataFrame(data)"
      ],
      "metadata": {
        "id": "cvkpqZhcXqnT"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "runAndReturn(searchManningsWithPage('2', 0))"
      ],
      "metadata": {
        "id": "SqWcXLXRFj1O",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "5a0bc187-a9d2-4325-eea2-0d775de6277b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-742ad499-add5-48c0-a826-971b5e3cd2d4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Ange Orange Teether 1pc</td>\n",
              "      <td>88.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Playtex Gentle Glide Tampon - Super Plus 18pcs</td>\n",
              "      <td>47.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Illuma Organic Stage 2 850g</td>\n",
              "      <td>500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Aptamil Essensis HMO Stage 2 900g</td>\n",
              "      <td>528.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Aptamil Essensis PHP Stage 2 900g</td>\n",
              "      <td>538.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>HiPP Organic Combiotic® HMP Toddler Milk Stage...</td>\n",
              "      <td>318.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>Nestlé® NAN® INFINIPRO® 1 Upgraded Formula 800g</td>\n",
              "      <td>438.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>Nestlé® NAN® INFINIPRO® 4 Upgraded Formula 800g</td>\n",
              "      <td>348.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>HiPP Organic Combiotic® HMP Infant Formula (st...</td>\n",
              "      <td>350.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>Abbott PediaSure 1+ Vanilla Flavor 850g</td>\n",
              "      <td>249.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>63 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-742ad499-add5-48c0-a826-971b5e3cd2d4')\"\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",
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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",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-742ad499-add5-48c0-a826-971b5e3cd2d4 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-742ad499-add5-48c0-a826-971b5e3cd2d4');\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",
              "  "
            ],
            "text/plain": [
              "                                                 name  price\n",
              "0                             Ange Orange Teether 1pc   88.0\n",
              "1      Playtex Gentle Glide Tampon - Super Plus 18pcs   47.9\n",
              "2                         Illuma Organic Stage 2 850g  500.0\n",
              "3                   Aptamil Essensis HMO Stage 2 900g  528.0\n",
              "4                   Aptamil Essensis PHP Stage 2 900g  538.0\n",
              "..                                                ...    ...\n",
              "58  HiPP Organic Combiotic® HMP Toddler Milk Stage...  318.0\n",
              "59    Nestlé® NAN® INFINIPRO® 1 Upgraded Formula 800g  438.0\n",
              "60    Nestlé® NAN® INFINIPRO® 4 Upgraded Formula 800g  348.0\n",
              "61  HiPP Organic Combiotic® HMP Infant Formula (st...  350.0\n",
              "62            Abbott PediaSure 1+ Vanilla Flavor 850g  249.0\n",
              "\n",
              "[63 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "manningsdf = pd.DataFrame(columns=['name', 'price'])"
      ],
      "metadata": {
        "id": "ROTvSff5cZ7e"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from time import sleep\n",
        "\n",
        "pageSequence = [i for i in range(0, total // 63 + 1)] # generate a list of page numbers\n",
        "np.random.shuffle(pageSequence) # shuffle the list\n",
        "\n",
        "for i in tqdm(pageSequence):\n",
        "  data = runAndReturn(searchManningsWithPage('2', i))\n",
        "  manningsdf = manningsdf.append(data, ignore_index=True)\n",
        "  sleep(np.random.uniform(2, 5)) # perform a random delay from 2 to 5 second"
      ],
      "metadata": {
        "id": "SGoQgqPDioj3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "manningsdf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "QWxaBiZ9jJtd",
        "outputId": "e8006495-332b-4df7-d9ab-1c8b85cf4876"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-7a756455-e384-4471-a0ff-0126695d303f\">\n",
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              "      <div>\n",
              "<style scoped>\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>price</th>\n",
              "      <th>source</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Ange Orange Teether 1pc</td>\n",
              "      <td>88.0</td>\n",
              "      <td>mannings</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Playtex Gentle Glide Tampon - Super Plus 18pcs</td>\n",
              "      <td>47.9</td>\n",
              "      <td>mannings</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Illuma Organic Stage 2 850g</td>\n",
              "      <td>500.0</td>\n",
              "      <td>mannings</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Aptamil Essensis HMO Stage 2 900g</td>\n",
              "      <td>528.0</td>\n",
              "      <td>mannings</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Aptamil Essensis PHP Stage 2 900g</td>\n",
              "      <td>538.0</td>\n",
              "      <td>mannings</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2893</th>\n",
              "      <td>HiPP Organic Combiotic® HMP Toddler Milk Stage...</td>\n",
              "      <td>318.0</td>\n",
              "      <td>mannings</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2894</th>\n",
              "      <td>Nestlé® NAN® INFINIPRO® 1 Upgraded Formula 800g</td>\n",
              "      <td>438.0</td>\n",
              "      <td>mannings</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2895</th>\n",
              "      <td>Nestlé® NAN® INFINIPRO® 4 Upgraded Formula 800g</td>\n",
              "      <td>348.0</td>\n",
              "      <td>mannings</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2896</th>\n",
              "      <td>HiPP Organic Combiotic® HMP Infant Formula (st...</td>\n",
              "      <td>350.0</td>\n",
              "      <td>mannings</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2897</th>\n",
              "      <td>Abbott PediaSure 1+ Vanilla Flavor 850g</td>\n",
              "      <td>249.0</td>\n",
              "      <td>mannings</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2898 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7a756455-e384-4471-a0ff-0126695d303f')\"\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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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7a756455-e384-4471-a0ff-0126695d303f 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-7a756455-e384-4471-a0ff-0126695d303f');\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",
              "  "
            ],
            "text/plain": [
              "                                                   name  price    source\n",
              "0                               Ange Orange Teether 1pc   88.0  mannings\n",
              "1        Playtex Gentle Glide Tampon - Super Plus 18pcs   47.9  mannings\n",
              "2                           Illuma Organic Stage 2 850g  500.0  mannings\n",
              "3                     Aptamil Essensis HMO Stage 2 900g  528.0  mannings\n",
              "4                     Aptamil Essensis PHP Stage 2 900g  538.0  mannings\n",
              "...                                                 ...    ...       ...\n",
              "2893  HiPP Organic Combiotic® HMP Toddler Milk Stage...  318.0  mannings\n",
              "2894    Nestlé® NAN® INFINIPRO® 1 Upgraded Formula 800g  438.0  mannings\n",
              "2895    Nestlé® NAN® INFINIPRO® 4 Upgraded Formula 800g  348.0  mannings\n",
              "2896  HiPP Organic Combiotic® HMP Infant Formula (st...  350.0  mannings\n",
              "2897            Abbott PediaSure 1+ Vanilla Flavor 850g  249.0  mannings\n",
              "\n",
              "[2898 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Make a backup first"
      ],
      "metadata": {
        "id": "in_-naYq_ZNQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "manningsdf.to_csv('mannings.csv')"
      ],
      "metadata": {
        "id": "GuzMPU1l6u2l"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://gist.githubusercontent.com/hkbu-kennycheng/8e719a45890bc9bcd47fa353598fcb1e/raw/8a7c3e74e327b47aa58dbccb3539660a276ac574/mannings.csv"
      ],
      "metadata": {
        "id": "PNtD1v-_laeo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "manningsdf = pd.read_csv('mannings.csv', index_col=0)"
      ],
      "metadata": {
        "id": "RiC2IODJlooE"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exercise"
      ],
      "metadata": {
        "id": "zep8oK6z4Cwy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "create a `source` colume in parknshap dataframe `df` with value `parknshop`"
      ],
      "metadata": {
        "id": "Hvup4DDLj6gP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "5J_URl_cjImt"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "create a `source` colume in mannings dataframe `manningsdf` with value `mannings`"
      ],
      "metadata": {
        "id": "iz4mTKzikSaj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "EwjTLpZ3kR2D"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Merging Mannings data with parknshop data in a single `df`"
      ],
      "metadata": {
        "id": "4AgwFTEVj1qd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Z-nX41a5kf7B",
        "outputId": "81988043-c83c-4716-a7d6-ffc17d246c88"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-04e377fa-5111-4eff-bfcd-edc6e3296220\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>price</th>\n",
              "      <th>source</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9inch Plastic Plates-clear</td>\n",
              "      <td>20.9</td>\n",
              "      <td>parknshop</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Select Brazilian Mid Joint Wing 2lb</td>\n",
              "      <td>50.9</td>\n",
              "      <td>parknshop</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mr Juicy 100% Orange Juice</td>\n",
              "      <td>20.5</td>\n",
              "      <td>parknshop</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Distilled Water(random Pick)</td>\n",
              "      <td>5.5</td>\n",
              "      <td>parknshop</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A4  Writing Pad</td>\n",
              "      <td>14.9</td>\n",
              "      <td>parknshop</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16610</th>\n",
              "      <td>HiPP Organic Combiotic® HMP Toddler Milk Stage...</td>\n",
              "      <td>318.0</td>\n",
              "      <td>mannings</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16611</th>\n",
              "      <td>Nestlé® NAN® INFINIPRO® 1 Upgraded Formula 800g</td>\n",
              "      <td>438.0</td>\n",
              "      <td>mannings</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16612</th>\n",
              "      <td>Nestlé® NAN® INFINIPRO® 4 Upgraded Formula 800g</td>\n",
              "      <td>348.0</td>\n",
              "      <td>mannings</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16613</th>\n",
              "      <td>HiPP Organic Combiotic® HMP Infant Formula (st...</td>\n",
              "      <td>350.0</td>\n",
              "      <td>mannings</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16614</th>\n",
              "      <td>Abbott PediaSure 1+ Vanilla Flavor 850g</td>\n",
              "      <td>249.0</td>\n",
              "      <td>mannings</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>16615 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-04e377fa-5111-4eff-bfcd-edc6e3296220')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-04e377fa-5111-4eff-bfcd-edc6e3296220 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-04e377fa-5111-4eff-bfcd-edc6e3296220');\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",
              "  "
            ],
            "text/plain": [
              "                                                    name  price     source\n",
              "0                             9inch Plastic Plates-clear   20.9  parknshop\n",
              "1                    Select Brazilian Mid Joint Wing 2lb   50.9  parknshop\n",
              "2                             Mr Juicy 100% Orange Juice   20.5  parknshop\n",
              "3                           Distilled Water(random Pick)    5.5  parknshop\n",
              "4                                        A4  Writing Pad   14.9  parknshop\n",
              "...                                                  ...    ...        ...\n",
              "16610  HiPP Organic Combiotic® HMP Toddler Milk Stage...  318.0   mannings\n",
              "16611    Nestlé® NAN® INFINIPRO® 1 Upgraded Formula 800g  438.0   mannings\n",
              "16612    Nestlé® NAN® INFINIPRO® 4 Upgraded Formula 800g  348.0   mannings\n",
              "16613  HiPP Organic Combiotic® HMP Infant Formula (st...  350.0   mannings\n",
              "16614            Abbott PediaSure 1+ Vanilla Flavor 850g  249.0   mannings\n",
              "\n",
              "[16615 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data visulization and feature extraction"
      ],
      "metadata": {
        "id": "eaJkTofsR0H9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['source'] == 'mannings'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bsN_Kr9OkrD0",
        "outputId": "3231d7ec-a165-47f8-acf5-de27bbfd95f7"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name      2898\n",
              "price     2898\n",
              "source    2898\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(df, x=\"price\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "lShCFEWy8F4V",
        "outputId": "15a66402-b78e-45fb-e01a-640b7450f9b2"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f7dac8ac250>"
            ]
          },
          "metadata": {},
          "execution_count": 95
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(df, x=\"price\", bins=[100, 200, 300, 400])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "ftTpK1TO8nqE",
        "outputId": "5b5d2201-4532-4e73-d91a-546cbbcc3935"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f7dac16bf50>"
            ]
          },
          "metadata": {},
          "execution_count": 96
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(df, x=\"price\", bins=[i for i in range(0, 500, 10)])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "KShDet1u-Eqy",
        "outputId": "ffc0e6d7-d850-4f80-e63c-3f849871b527"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f7dac162cd0>"
            ]
          },
          "metadata": {},
          "execution_count": 97
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Price range"
      ],
      "metadata": {
        "id": "XpGfr59ySH7U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bins = [i for i in range(0, 500, 10)]\n",
        "labels = [f'{i-10}-{i-0.01}' for i in range(10, 500, 10)]\n",
        "df['price_group10'] = pd.cut(df.price, bins, labels = labels,include_lowest = True)\n",
        "df"
      ],
      "metadata": {
        "id": "-rjdQRyryETy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "0965257a-52af-40f1-abfa-f8efedd6a807"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-a39c0ab8-e7f9-415b-bb21-ad09263a7a22\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>price</th>\n",
              "      <th>source</th>\n",
              "      <th>price_group10</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9inch Plastic Plates-clear</td>\n",
              "      <td>20.9</td>\n",
              "      <td>parknshop</td>\n",
              "      <td>20-29.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Select Brazilian Mid Joint Wing 2lb</td>\n",
              "      <td>50.9</td>\n",
              "      <td>parknshop</td>\n",
              "      <td>50-59.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mr Juicy 100% Orange Juice</td>\n",
              "      <td>20.5</td>\n",
              "      <td>parknshop</td>\n",
              "      <td>20-29.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Distilled Water(random Pick)</td>\n",
              "      <td>5.5</td>\n",
              "      <td>parknshop</td>\n",
              "      <td>0-9.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A4  Writing Pad</td>\n",
              "      <td>14.9</td>\n",
              "      <td>parknshop</td>\n",
              "      <td>10-19.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16610</th>\n",
              "      <td>HiPP Organic Combiotic® HMP Toddler Milk Stage...</td>\n",
              "      <td>318.0</td>\n",
              "      <td>mannings</td>\n",
              "      <td>310-319.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16611</th>\n",
              "      <td>Nestlé® NAN® INFINIPRO® 1 Upgraded Formula 800g</td>\n",
              "      <td>438.0</td>\n",
              "      <td>mannings</td>\n",
              "      <td>430-439.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16612</th>\n",
              "      <td>Nestlé® NAN® INFINIPRO® 4 Upgraded Formula 800g</td>\n",
              "      <td>348.0</td>\n",
              "      <td>mannings</td>\n",
              "      <td>340-349.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16613</th>\n",
              "      <td>HiPP Organic Combiotic® HMP Infant Formula (st...</td>\n",
              "      <td>350.0</td>\n",
              "      <td>mannings</td>\n",
              "      <td>340-349.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16614</th>\n",
              "      <td>Abbott PediaSure 1+ Vanilla Flavor 850g</td>\n",
              "      <td>249.0</td>\n",
              "      <td>mannings</td>\n",
              "      <td>240-249.99</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>16615 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a39c0ab8-e7f9-415b-bb21-ad09263a7a22')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
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              "\n",
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              "          document.querySelector('#df-a39c0ab8-e7f9-415b-bb21-ad09263a7a22 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-a39c0ab8-e7f9-415b-bb21-ad09263a7a22');\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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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                    name  ...  price_group10\n",
              "0                             9inch Plastic Plates-clear  ...       20-29.99\n",
              "1                    Select Brazilian Mid Joint Wing 2lb  ...       50-59.99\n",
              "2                             Mr Juicy 100% Orange Juice  ...       20-29.99\n",
              "3                           Distilled Water(random Pick)  ...         0-9.99\n",
              "4                                        A4  Writing Pad  ...       10-19.99\n",
              "...                                                  ...  ...            ...\n",
              "16610  HiPP Organic Combiotic® HMP Toddler Milk Stage...  ...     310-319.99\n",
              "16611    Nestlé® NAN® INFINIPRO® 1 Upgraded Formula 800g  ...     430-439.99\n",
              "16612    Nestlé® NAN® INFINIPRO® 4 Upgraded Formula 800g  ...     340-349.99\n",
              "16613  HiPP Organic Combiotic® HMP Infant Formula (st...  ...     340-349.99\n",
              "16614            Abbott PediaSure 1+ Vanilla Flavor 850g  ...     240-249.99\n",
              "\n",
              "[16615 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bins = [i for i in range(0, 500, 100)]\n",
        "labels = [f'{i-100}-{i-0.01}' for i in range(100, 500, 100)]\n",
        "df['price_group100'] = pd.cut(df.price, bins, labels = labels,include_lowest = True)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "id": "_o6klzTRzBT5",
        "outputId": "fe1e2975-fbed-47d3-a0ed-1290827d89fb"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "<style scoped>\n",
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              "      <th>name</th>\n",
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              "      <th>2</th>\n",
              "      <td>Mr Juicy 100% Orange Juice</td>\n",
              "      <td>20.5</td>\n",
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              "      <td>0-99.99</td>\n",
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              "      <th>3</th>\n",
              "      <td>Distilled Water(random Pick)</td>\n",
              "      <td>5.5</td>\n",
              "      <td>parknshop</td>\n",
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              "      <td>0-99.99</td>\n",
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              "    <tr>\n",
              "      <th>16610</th>\n",
              "      <td>HiPP Organic Combiotic® HMP Toddler Milk Stage...</td>\n",
              "      <td>318.0</td>\n",
              "      <td>mannings</td>\n",
              "      <td>310-319.99</td>\n",
              "      <td>300-399.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16611</th>\n",
              "      <td>Nestlé® NAN® INFINIPRO® 1 Upgraded Formula 800g</td>\n",
              "      <td>438.0</td>\n",
              "      <td>mannings</td>\n",
              "      <td>430-439.99</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>16612</th>\n",
              "      <td>Nestlé® NAN® INFINIPRO® 4 Upgraded Formula 800g</td>\n",
              "      <td>348.0</td>\n",
              "      <td>mannings</td>\n",
              "      <td>340-349.99</td>\n",
              "      <td>300-399.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16613</th>\n",
              "      <td>HiPP Organic Combiotic® HMP Infant Formula (st...</td>\n",
              "      <td>350.0</td>\n",
              "      <td>mannings</td>\n",
              "      <td>340-349.99</td>\n",
              "      <td>300-399.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16614</th>\n",
              "      <td>Abbott PediaSure 1+ Vanilla Flavor 850g</td>\n",
              "      <td>249.0</td>\n",
              "      <td>mannings</td>\n",
              "      <td>240-249.99</td>\n",
              "      <td>200-299.99</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>16615 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-35c44d2b-7a61-4a41-879b-7ccb032d72fe')\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-35c44d2b-7a61-4a41-879b-7ccb032d72fe');\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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                    name  ...  price_group100\n",
              "0                             9inch Plastic Plates-clear  ...         0-99.99\n",
              "1                    Select Brazilian Mid Joint Wing 2lb  ...         0-99.99\n",
              "2                             Mr Juicy 100% Orange Juice  ...         0-99.99\n",
              "3                           Distilled Water(random Pick)  ...         0-99.99\n",
              "4                                        A4  Writing Pad  ...         0-99.99\n",
              "...                                                  ...  ...             ...\n",
              "16610  HiPP Organic Combiotic® HMP Toddler Milk Stage...  ...      300-399.99\n",
              "16611    Nestlé® NAN® INFINIPRO® 1 Upgraded Formula 800g  ...             NaN\n",
              "16612    Nestlé® NAN® INFINIPRO® 4 Upgraded Formula 800g  ...      300-399.99\n",
              "16613  HiPP Organic Combiotic® HMP Infant Formula (st...  ...      300-399.99\n",
              "16614            Abbott PediaSure 1+ Vanilla Flavor 850g  ...      200-299.99\n",
              "\n",
              "[16615 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='price_group100', hue='source', data=df)"
      ],
      "metadata": {
        "id": "avOW9mLN-gN5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "outputId": "aed9a56b-9a18-4841-a652-31e399dd368c"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7dac0c6190>"
            ]
          },
          "metadata": {},
          "execution_count": 100
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='price_group10', hue='source', data=df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "tWMoetdLzZX5",
        "outputId": "9796adfe-8193-4ea4-c226-65d363c432e8"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7dabf97ad0>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['price_cent'] = df['price'] - np.floor(df['price'])\n",
        "df['price_cent'] = np.floor(df['price_cent'] * 10)\n"
      ],
      "metadata": {
        "id": "zFiZt4Xg0liJ"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(df, x='price_group100', hue='price_cent', stat='count', binwidth=1, multiple='stack')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "e8E9L9bbxVqz",
        "outputId": "34cfb384-0c63-4ce9-afbd-61ab30a0c169"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7dac07f710>"
            ]
          },
          "metadata": {},
          "execution_count": 103
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set(rc={'figure.figsize':(20,10)})\n",
        "sns.displot(df, x='price_cent', hue='price_group100', kind='kde', bw_adjust=2, multiple='stack')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "L-CawRCd8Q7m",
        "outputId": "1a646bda-20c8-48e8-9499-892b4d10f4cc"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f7dac118c90>"
            ]
          },
          "metadata": {},
          "execution_count": 104
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 462.975x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(y=df['price_cent'], x=df['price_group100'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "CRmxQfVwE62A",
        "outputId": "a5f2bf47-d0e9-4d91-f1ce-00ebba8cacdf"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7dabbf4590>"
            ]
          },
          "metadata": {},
          "execution_count": 105
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(y=df['price_cent'], x=df['price_group10'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "KyWUWNoEFNbK",
        "outputId": "de2160e3-4d3d-4f2a-bc9d-f04f33cfe85e"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7dabd45d90>"
            ]
          },
          "metadata": {},
          "execution_count": 106
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['price_over_cent'] = round(df['price']/df['price_cent'])"
      ],
      "metadata": {
        "id": "-P5S3ZpDFdAJ"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxenplot(x=df['price_group100'], y=df['price_over_cent'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "id": "rq5JQPypFnLZ",
        "outputId": "ee1f854a-e873-4b7c-a0c9-3e2efa2a8ed7"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7dabb50250>"
            ]
          },
          "metadata": {},
          "execution_count": 108
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Organic"
      ],
      "metadata": {
        "id": "ND_9q-ABo5EQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['organic'] = df['name'].str.lower()\n",
        "df['organic'] = df['organic'].str.count('organic')\n",
        "df['organic'] = df['organic'].clip(0, 1)\n",
        "df['organic'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-yt5sGyyo4eR",
        "outputId": "bbc9604b-d034-48e3-aed9-65287c6874a8"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    16615.000000\n",
              "mean         0.050737\n",
              "std          0.219467\n",
              "min          0.000000\n",
              "25%          0.000000\n",
              "50%          0.000000\n",
              "75%          0.000000\n",
              "max          1.000000\n",
              "Name: organic, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exercise\n",
        "\n",
        "Extract more features from name, and provide some exploratory data analysis."
      ],
      "metadata": {
        "id": "Ufiag8k4GBlg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "KNwrhBCmxw4w"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}