mining.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "SHSmh-JwC6xf"
   },
   "outputs": [],
   "source": [
    "!pip install dputils -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "4kORcbpADGVI"
   },
   "outputs": [],
   "source": [
    "url = \"https://digipodium.com/mining-demo.html\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "4pySploSDZbI"
   },
   "outputs": [],
   "source": [
    "from dputils.scrape import Scraper, Tag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "wTZ_VILqDf-9"
   },
   "outputs": [],
   "source": [
    "scr = Scraper(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "vkDCNN-YDq9F"
   },
   "outputs": [],
   "source": [
    "result = scr.get_repeating_date_from_page(\n",
    "    target=Tag(cls='item-list'),\n",
    "    items=Tag(cls=\"card\"),\n",
    "    title = Tag('h4'),\n",
    "    price = Tag('h1', cls=\"pricing-card-title\"),\n",
    "    oprice = Tag('li', cls='o-price')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "uB8qN47PFaOe"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 801
    },
    "collapsed": true,
    "id": "4xOBvdA2GQdV",
    "outputId": "88dbf4a3-98dc-4c4f-9382-d885ba1066ea"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>title</th>\n",
       "      <th>price</th>\n",
       "      <th>oprice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LuxoWatch</td>\n",
       "      <td>₹5949.82</td>\n",
       "      <td>₹7436.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SuperPhone X</td>\n",
       "      <td>₹67118.07</td>\n",
       "      <td>₹79044.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Miracle Blender</td>\n",
       "      <td>₹3689.88</td>\n",
       "      <td>₹5273.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Quantum Headphones</td>\n",
       "      <td>₹9709.69</td>\n",
       "      <td>₹12922.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SmartFridge</td>\n",
       "      <td>₹89699.05</td>\n",
       "      <td>₹109295.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Galactic Glasses</td>\n",
       "      <td>₹2234.52</td>\n",
       "      <td>₹2488.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PowerJuicer</td>\n",
       "      <td>₹6651.27</td>\n",
       "      <td>₹8536.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>iSpectre Laptop</td>\n",
       "      <td>₹119647.11</td>\n",
       "      <td>₹149208.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>HealthBand</td>\n",
       "      <td>₹5228.64</td>\n",
       "      <td>₹5949.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MaxiSound Speaker</td>\n",
       "      <td>₹14908.25</td>\n",
       "      <td>₹22972.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>UltraSound Toothbrush</td>\n",
       "      <td>₹2456.50</td>\n",
       "      <td>₹2889.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SuperFit Smartwatch</td>\n",
       "      <td>₹3679.99</td>\n",
       "      <td>₹4899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GoPro Hero 9</td>\n",
       "      <td>₹28999.00</td>\n",
       "      <td>₹36124.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>AirPods Pro</td>\n",
       "      <td>₹12780.50</td>\n",
       "      <td>₹14199.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Fitness Treadmill</td>\n",
       "      <td>₹38999.00</td>\n",
       "      <td>₹45883.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Bluetooth Headphones</td>\n",
       "      <td>₹1649.99</td>\n",
       "      <td>₹1833.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Smart Thermostat</td>\n",
       "      <td>₹2999.00</td>\n",
       "      <td>₹3749.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Automatic Coffee Maker</td>\n",
       "      <td>₹6299.99</td>\n",
       "      <td>₹7411.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Wireless Charger</td>\n",
       "      <td>₹1499.50</td>\n",
       "      <td>₹1665.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bluetooth Headphones</td>\n",
       "      <td>₹1649.99</td>\n",
       "      <td>₹1833.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Smart Thermostat</td>\n",
       "      <td>₹2999.00</td>\n",
       "      <td>₹3749.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Automatic Coffee Maker</td>\n",
       "      <td>₹6299.99</td>\n",
       "      <td>₹7411.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Wireless Charger</td>\n",
       "      <td>₹1499.50</td>\n",
       "      <td>₹1665.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Wireless Bluetooth Earbuds</td>\n",
       "      <td>₹899.99</td>\n",
       "      <td>₹999.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         title       price      oprice\n",
       "0                   LuxoWatch     ₹5949.82    ₹7436.88\n",
       "1                 SuperPhone X   ₹67118.07   ₹79044.38\n",
       "2              Miracle Blender    ₹3689.88    ₹5273.51\n",
       "3           Quantum Headphones    ₹9709.69   ₹12922.74\n",
       "4                  SmartFridge   ₹89699.05  ₹109295.41\n",
       "5             Galactic Glasses    ₹2234.52    ₹2488.20\n",
       "6                  PowerJuicer    ₹6651.27    ₹8536.48\n",
       "7              iSpectre Laptop  ₹119647.11  ₹149208.92\n",
       "8                   HealthBand    ₹5228.64    ₹5949.82\n",
       "9            MaxiSound Speaker   ₹14908.25   ₹22972.57\n",
       "10       UltraSound Toothbrush    ₹2456.50    ₹2889.41\n",
       "11         SuperFit Smartwatch    ₹3679.99       ₹4899\n",
       "12                GoPro Hero 9   ₹28999.00   ₹36124.99\n",
       "13                 AirPods Pro   ₹12780.50   ₹14199.44\n",
       "14           Fitness Treadmill   ₹38999.00   ₹45883.50\n",
       "15        Bluetooth Headphones    ₹1649.99    ₹1833.32\n",
       "16            Smart Thermostat    ₹2999.00    ₹3749.00\n",
       "17      Automatic Coffee Maker    ₹6299.99    ₹7411.75\n",
       "18            Wireless Charger    ₹1499.50    ₹1665.00\n",
       "19        Bluetooth Headphones    ₹1649.99    ₹1833.32\n",
       "20            Smart Thermostat    ₹2999.00    ₹3749.00\n",
       "21      Automatic Coffee Maker    ₹6299.99    ₹7411.75\n",
       "22            Wireless Charger    ₹1499.50    ₹1665.00\n",
       "23  Wireless Bluetooth Earbuds     ₹899.99     ₹999.99"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(result)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 801
    },
    "collapsed": true,
    "id": "yYQ4CaTpGdyr",
    "outputId": "27d4f9ec-1019-412a-e9d8-659e78f1ad26"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>title</th>\n",
       "      <th>price</th>\n",
       "      <th>oprice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LuxoWatch</td>\n",
       "      <td>5949.82</td>\n",
       "      <td>₹7436.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SuperPhone X</td>\n",
       "      <td>67118.07</td>\n",
       "      <td>₹79044.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Miracle Blender</td>\n",
       "      <td>3689.88</td>\n",
       "      <td>₹5273.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Quantum Headphones</td>\n",
       "      <td>9709.69</td>\n",
       "      <td>₹12922.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SmartFridge</td>\n",
       "      <td>89699.05</td>\n",
       "      <td>₹109295.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Galactic Glasses</td>\n",
       "      <td>2234.52</td>\n",
       "      <td>₹2488.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PowerJuicer</td>\n",
       "      <td>6651.27</td>\n",
       "      <td>₹8536.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>iSpectre Laptop</td>\n",
       "      <td>119647.11</td>\n",
       "      <td>₹149208.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>HealthBand</td>\n",
       "      <td>5228.64</td>\n",
       "      <td>₹5949.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MaxiSound Speaker</td>\n",
       "      <td>14908.25</td>\n",
       "      <td>₹22972.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>UltraSound Toothbrush</td>\n",
       "      <td>2456.50</td>\n",
       "      <td>₹2889.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SuperFit Smartwatch</td>\n",
       "      <td>3679.99</td>\n",
       "      <td>₹4899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GoPro Hero 9</td>\n",
       "      <td>28999.00</td>\n",
       "      <td>₹36124.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>AirPods Pro</td>\n",
       "      <td>12780.50</td>\n",
       "      <td>₹14199.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Fitness Treadmill</td>\n",
       "      <td>38999.00</td>\n",
       "      <td>₹45883.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Bluetooth Headphones</td>\n",
       "      <td>1649.99</td>\n",
       "      <td>₹1833.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Smart Thermostat</td>\n",
       "      <td>2999.00</td>\n",
       "      <td>₹3749.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Automatic Coffee Maker</td>\n",
       "      <td>6299.99</td>\n",
       "      <td>₹7411.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Wireless Charger</td>\n",
       "      <td>1499.50</td>\n",
       "      <td>₹1665.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bluetooth Headphones</td>\n",
       "      <td>1649.99</td>\n",
       "      <td>₹1833.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Smart Thermostat</td>\n",
       "      <td>2999.00</td>\n",
       "      <td>₹3749.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Automatic Coffee Maker</td>\n",
       "      <td>6299.99</td>\n",
       "      <td>₹7411.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Wireless Charger</td>\n",
       "      <td>1499.50</td>\n",
       "      <td>₹1665.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Wireless Bluetooth Earbuds</td>\n",
       "      <td>899.99</td>\n",
       "      <td>₹999.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         title      price      oprice\n",
       "0                   LuxoWatch     5949.82    ₹7436.88\n",
       "1                 SuperPhone X   67118.07   ₹79044.38\n",
       "2              Miracle Blender    3689.88    ₹5273.51\n",
       "3           Quantum Headphones    9709.69   ₹12922.74\n",
       "4                  SmartFridge   89699.05  ₹109295.41\n",
       "5             Galactic Glasses    2234.52    ₹2488.20\n",
       "6                  PowerJuicer    6651.27    ₹8536.48\n",
       "7              iSpectre Laptop  119647.11  ₹149208.92\n",
       "8                   HealthBand    5228.64    ₹5949.82\n",
       "9            MaxiSound Speaker   14908.25   ₹22972.57\n",
       "10       UltraSound Toothbrush    2456.50    ₹2889.41\n",
       "11         SuperFit Smartwatch    3679.99       ₹4899\n",
       "12                GoPro Hero 9   28999.00   ₹36124.99\n",
       "13                 AirPods Pro   12780.50   ₹14199.44\n",
       "14           Fitness Treadmill   38999.00   ₹45883.50\n",
       "15        Bluetooth Headphones    1649.99    ₹1833.32\n",
       "16            Smart Thermostat    2999.00    ₹3749.00\n",
       "17      Automatic Coffee Maker    6299.99    ₹7411.75\n",
       "18            Wireless Charger    1499.50    ₹1665.00\n",
       "19        Bluetooth Headphones    1649.99    ₹1833.32\n",
       "20            Smart Thermostat    2999.00    ₹3749.00\n",
       "21      Automatic Coffee Maker    6299.99    ₹7411.75\n",
       "22            Wireless Charger    1499.50    ₹1665.00\n",
       "23  Wireless Bluetooth Earbuds     899.99     ₹999.99"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.price = df.price.apply(lambda p:p[1:]).astype(float)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 801
    },
    "collapsed": true,
    "id": "DcKwimJwHGmS",
    "outputId": "dea37089-059b-4744-ddfb-b321012a99b5"
   },
   "outputs": [
    {
     "data": {
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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>title</th>\n",
       "      <th>price</th>\n",
       "      <th>oprice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LuxoWatch</td>\n",
       "      <td>5949.82</td>\n",
       "      <td>7436.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SuperPhone X</td>\n",
       "      <td>67118.07</td>\n",
       "      <td>79044.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Miracle Blender</td>\n",
       "      <td>3689.88</td>\n",
       "      <td>5273.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Quantum Headphones</td>\n",
       "      <td>9709.69</td>\n",
       "      <td>12922.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SmartFridge</td>\n",
       "      <td>89699.05</td>\n",
       "      <td>109295.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Galactic Glasses</td>\n",
       "      <td>2234.52</td>\n",
       "      <td>2488.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PowerJuicer</td>\n",
       "      <td>6651.27</td>\n",
       "      <td>8536.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>iSpectre Laptop</td>\n",
       "      <td>119647.11</td>\n",
       "      <td>149208.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>HealthBand</td>\n",
       "      <td>5228.64</td>\n",
       "      <td>5949.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MaxiSound Speaker</td>\n",
       "      <td>14908.25</td>\n",
       "      <td>22972.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>UltraSound Toothbrush</td>\n",
       "      <td>2456.50</td>\n",
       "      <td>2889.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SuperFit Smartwatch</td>\n",
       "      <td>3679.99</td>\n",
       "      <td>4899.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GoPro Hero 9</td>\n",
       "      <td>28999.00</td>\n",
       "      <td>36124.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>AirPods Pro</td>\n",
       "      <td>12780.50</td>\n",
       "      <td>14199.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Fitness Treadmill</td>\n",
       "      <td>38999.00</td>\n",
       "      <td>45883.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Bluetooth Headphones</td>\n",
       "      <td>1649.99</td>\n",
       "      <td>1833.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Smart Thermostat</td>\n",
       "      <td>2999.00</td>\n",
       "      <td>3749.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Automatic Coffee Maker</td>\n",
       "      <td>6299.99</td>\n",
       "      <td>7411.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Wireless Charger</td>\n",
       "      <td>1499.50</td>\n",
       "      <td>1665.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bluetooth Headphones</td>\n",
       "      <td>1649.99</td>\n",
       "      <td>1833.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Smart Thermostat</td>\n",
       "      <td>2999.00</td>\n",
       "      <td>3749.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Automatic Coffee Maker</td>\n",
       "      <td>6299.99</td>\n",
       "      <td>7411.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Wireless Charger</td>\n",
       "      <td>1499.50</td>\n",
       "      <td>1665.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Wireless Bluetooth Earbuds</td>\n",
       "      <td>899.99</td>\n",
       "      <td>999.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         title      price     oprice\n",
       "0                   LuxoWatch     5949.82    7436.88\n",
       "1                 SuperPhone X   67118.07   79044.38\n",
       "2              Miracle Blender    3689.88    5273.51\n",
       "3           Quantum Headphones    9709.69   12922.74\n",
       "4                  SmartFridge   89699.05  109295.41\n",
       "5             Galactic Glasses    2234.52    2488.20\n",
       "6                  PowerJuicer    6651.27    8536.48\n",
       "7              iSpectre Laptop  119647.11  149208.92\n",
       "8                   HealthBand    5228.64    5949.82\n",
       "9            MaxiSound Speaker   14908.25   22972.57\n",
       "10       UltraSound Toothbrush    2456.50    2889.41\n",
       "11         SuperFit Smartwatch    3679.99    4899.00\n",
       "12                GoPro Hero 9   28999.00   36124.99\n",
       "13                 AirPods Pro   12780.50   14199.44\n",
       "14           Fitness Treadmill   38999.00   45883.50\n",
       "15        Bluetooth Headphones    1649.99    1833.32\n",
       "16            Smart Thermostat    2999.00    3749.00\n",
       "17      Automatic Coffee Maker    6299.99    7411.75\n",
       "18            Wireless Charger    1499.50    1665.00\n",
       "19        Bluetooth Headphones    1649.99    1833.32\n",
       "20            Smart Thermostat    2999.00    3749.00\n",
       "21      Automatic Coffee Maker    6299.99    7411.75\n",
       "22            Wireless Charger    1499.50    1665.00\n",
       "23  Wireless Bluetooth Earbuds     899.99     999.99"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.oprice = df.oprice.apply(lambda p:p[1:]).astype(float)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 639
    },
    "collapsed": true,
    "id": "0P7J1RdQ_6LJ",
    "outputId": "4ec017d6-39c8-428b-d2af-56bea8777e91"
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'google.colab'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[10], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgoogle\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcolab\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m sheets\n\u001b[0;32m      2\u001b[0m sheet \u001b[38;5;241m=\u001b[39m sheets\u001b[38;5;241m.\u001b[39mInteractiveSheet(df\u001b[38;5;241m=\u001b[39mdf)\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'google.colab'"
     ]
    }
   ],
   "source": [
    "from google.colab import sheets\n",
    "sheet = sheets.InteractiveSheet(df=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RH6QH709r-oU",
    "outputId": "a9de7651-9943-4538-8bf1-8dafa657b9b3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: plotly in c:\\users\\lenovo\\miniconda3\\lib\\site-packages (5.24.1)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in c:\\users\\lenovo\\miniconda3\\lib\\site-packages (from plotly) (9.0.0)\n",
      "Requirement already satisfied: packaging in c:\\users\\lenovo\\miniconda3\\lib\\site-packages (from plotly) (24.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "IZm056koseez"
   },
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 617
    },
    "id": "z5w56ENYH1MW",
    "outputId": "52f169bd-03c9-4ea4-852e-c6e6f4313bfe"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[16], line 13\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Bar Chart: Prices vs. Original Prices\u001b[39;00m\n\u001b[0;32m      2\u001b[0m bar_fig \u001b[38;5;241m=\u001b[39m px\u001b[38;5;241m.\u001b[39mbar(\n\u001b[0;32m      3\u001b[0m     df,\n\u001b[0;32m      4\u001b[0m     x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtitle\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     11\u001b[0m     height\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m600\u001b[39m,\n\u001b[0;32m     12\u001b[0m )\n\u001b[1;32m---> 13\u001b[0m \u001b[43mbar_fig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\LENOVO\\miniconda3\\Lib\\site-packages\\plotly\\basedatatypes.py:3410\u001b[0m, in \u001b[0;36mBaseFigure.show\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   3377\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   3378\u001b[0m \u001b[38;5;124;03mShow a figure using either the default renderer(s) or the renderer(s)\u001b[39;00m\n\u001b[0;32m   3379\u001b[0m \u001b[38;5;124;03mspecified by the renderer argument\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   3406\u001b[0m \u001b[38;5;124;03mNone\u001b[39;00m\n\u001b[0;32m   3407\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   3408\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mplotly\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpio\u001b[39;00m\n\u001b[1;32m-> 3410\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\LENOVO\\miniconda3\\Lib\\site-packages\\plotly\\io\\_renderers.py:394\u001b[0m, in \u001b[0;36mshow\u001b[1;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[0;32m    389\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    390\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires ipython but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    391\u001b[0m         )\n\u001b[0;32m    393\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m nbformat \u001b[38;5;129;01mor\u001b[39;00m Version(nbformat\u001b[38;5;241m.\u001b[39m__version__) \u001b[38;5;241m<\u001b[39m Version(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m4.2.0\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m--> 394\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    395\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires nbformat>=4.2.0 but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    396\u001b[0m         )\n\u001b[0;32m    398\u001b[0m     ipython_display\u001b[38;5;241m.\u001b[39mdisplay(bundle, raw\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m    400\u001b[0m \u001b[38;5;66;03m# external renderers\u001b[39;00m\n",
      "\u001b[1;31mValueError\u001b[0m: Mime type rendering requires nbformat>=4.2.0 but it is not installed"
     ]
    }
   ],
   "source": [
    "# Bar Chart: Prices vs. Original Prices\n",
    "bar_fig = px.bar(\n",
    "    df,\n",
    "    x=\"title\",\n",
    "    y=[\"price\", \"oprice\"],\n",
    "    title=\"Current Prices vs. Original Prices of Products\",\n",
    "    labels={\"value\": \"Price (₹)\", \"title\": \"Products\"},\n",
    "    barmode=\"group\",\n",
    "    template=\"plotly_dark\",\n",
    "    width=800,\n",
    "    height=600,\n",
    ")\n",
    "bar_fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 617
    },
    "id": "MWwnyV7ir9Kf",
    "outputId": "2c2ffc73-d1cb-445d-e8b6-b5ccc6a8c964"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[17], line 12\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Pie Chart: Percentage Share of Product Prices\u001b[39;00m\n\u001b[0;32m      2\u001b[0m pie_fig \u001b[38;5;241m=\u001b[39m px\u001b[38;5;241m.\u001b[39mpie(\n\u001b[0;32m      3\u001b[0m     df,\n\u001b[0;32m      4\u001b[0m     values\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprice\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     10\u001b[0m     height\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m600\u001b[39m,\n\u001b[0;32m     11\u001b[0m )\n\u001b[1;32m---> 12\u001b[0m \u001b[43mpie_fig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\LENOVO\\miniconda3\\Lib\\site-packages\\plotly\\basedatatypes.py:3410\u001b[0m, in \u001b[0;36mBaseFigure.show\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   3377\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   3378\u001b[0m \u001b[38;5;124;03mShow a figure using either the default renderer(s) or the renderer(s)\u001b[39;00m\n\u001b[0;32m   3379\u001b[0m \u001b[38;5;124;03mspecified by the renderer argument\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   3406\u001b[0m \u001b[38;5;124;03mNone\u001b[39;00m\n\u001b[0;32m   3407\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   3408\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mplotly\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpio\u001b[39;00m\n\u001b[1;32m-> 3410\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\LENOVO\\miniconda3\\Lib\\site-packages\\plotly\\io\\_renderers.py:394\u001b[0m, in \u001b[0;36mshow\u001b[1;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[0;32m    389\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    390\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires ipython but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    391\u001b[0m         )\n\u001b[0;32m    393\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m nbformat \u001b[38;5;129;01mor\u001b[39;00m Version(nbformat\u001b[38;5;241m.\u001b[39m__version__) \u001b[38;5;241m<\u001b[39m Version(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m4.2.0\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m--> 394\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    395\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires nbformat>=4.2.0 but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    396\u001b[0m         )\n\u001b[0;32m    398\u001b[0m     ipython_display\u001b[38;5;241m.\u001b[39mdisplay(bundle, raw\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m    400\u001b[0m \u001b[38;5;66;03m# external renderers\u001b[39;00m\n",
      "\u001b[1;31mValueError\u001b[0m: Mime type rendering requires nbformat>=4.2.0 but it is not installed"
     ]
    }
   ],
   "source": [
    "# Pie Chart: Percentage Share of Product Prices\n",
    "pie_fig = px.pie(\n",
    "    df,\n",
    "    values=\"price\",\n",
    "    names=\"title\",\n",
    "    title=\"Percentage Share of Product Prices\",\n",
    "    hole=0.3,\n",
    "    template=\"plotly_dark\",\n",
    "    width=800,\n",
    "    height=600,\n",
    ")\n",
    "pie_fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "id": "As5UJSCrsxrW",
    "outputId": "655c032d-f860-4b68-a011-1d27ad124422"
   },
   "outputs": [
    {
     "data": {
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   ],
   "source": [
    "# Prepare data for the bar chart race\n",
    "# Add a \"time\" column to simulate progression\n",
    "steps = 15  # Number of animation steps\n",
    "frames = []\n",
    "\n",
    "# Interpolate values over steps to simulate animation\n",
    "for i in range(steps + 1):\n",
    "    frame = df.copy()\n",
    "    frame[\"step\"] = i  # Add a time-like column\n",
    "    frame[\"price_progress\"] = frame[\"price\"] * (i / steps)\n",
    "    frame[\"oprice_progress\"] = frame[\"oprice\"] * (i / steps)\n",
    "    frames.append(frame)\n",
    "\n",
    "# Combine all frames\n",
    "race_df = pd.concat(frames)\n",
    "\n",
    "# Melt data for animation\n",
    "race_df_melted = pd.melt(\n",
    "    race_df,\n",
    "    id_vars=[\"title\", \"step\"],\n",
    "    value_vars=[\"price_progress\", \"oprice_progress\"],\n",
    "    var_name=\"Price Type\",\n",
    "    value_name=\"Value\",\n",
    ")\n",
    "\n",
    "# Map human-readable labels for Price Type\n",
    "race_df_melted[\"Price Type\"] = race_df_melted[\"Price Type\"].replace(\n",
    "    {\"price_progress\": \"Price\", \"oprice_progress\": \"Original Price\"}\n",
    ")\n",
    "\n",
    "# Create animated bar chart\n",
    "bar_race_fig = px.bar(\n",
    "    race_df_melted,\n",
    "    x=\"Value\",\n",
    "    y=\"title\",\n",
    "    color=\"Price Type\",\n",
    "    orientation=\"h\",\n",
    "    animation_frame=\"step\",\n",
    "    title=\"Bar Chart Race: Price vs Original Price\",\n",
    "    labels={\"Value\": \"Price (₹)\", \"title\": \"Product\"},\n",
    "    color_discrete_map={\"Price\": \"blue\", \"Original Price\": \"red\"}\n",
    ")\n",
    "bar_race_fig.update_layout(yaxis={\"categoryorder\": \"total ascending\"})\n",
    "bar_race_fig.show()\n"
   ]
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