18IT036_Web_Scraping.ipynb

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  "metadata": {
    "colab": {
      "name": "18IT036_Web_Scraping.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "-JsQge8Nie2_"
      },
      "source": [
        "import requests\n",
        "import csv\n",
        "from bs4 import BeautifulSoup\n",
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QxYISK8xorfB"
      },
      "source": [
        "url=\"https://www.flipkart.com/search?q=phones&otracker=search&otracker1=search&marketplace=FLIPKART&as-show=on&as=off\"\n",
        "response = requests.get(url)\n",
        "htmlcontent = response.content\n",
        "soup = BeautifulSoup(htmlcontent,\"html.parser\")"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z38Zi_7GlsdM",
        "outputId": "115cab66-068b-48a4-e853-686223370fd8"
      },
      "source": [
        "products=[]\n",
        "prices=[]\n",
        "product=soup.find('div',attrs={'class':'_4rR01T'})\n",
        "print(product.text)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "realme C20 (Cool Grey, 32 GB)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Cy45pQWmAwL"
      },
      "source": [
        "for a in soup.findAll('a', attrs={'class':'_1fQZEK'}):\n",
        "  name=a.find('div',attrs={'class':'_4rR01T'})\n",
        "  price=a.find('div',attrs={'class':'_30jeq3 _1_WHN1'})\n",
        "  products.append(name.text)\n",
        "  prices.append(price.text)\n",
        "  "
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "00KM5XtKDMXP"
      },
      "source": [
        "final=[]\n",
        "for i in prices:\n",
        "    i=i[1:]\n",
        "    final.append(i)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "9t6PX7gVmGHj",
        "outputId": "6a85da09-3927-4aa6-841b-b1457295a008"
      },
      "source": [
        "df = pd.DataFrame({'Product Name':products,'Prices':final})\n",
        "df.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Product Name</th>\n",
              "      <th>Prices</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>realme C20 (Cool Grey, 32 GB)</td>\n",
              "      <td>6,999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>realme C20 (Cool Blue, 32 GB)</td>\n",
              "      <td>6,999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>REDMI 9i (Nature Green, 64 GB)</td>\n",
              "      <td>8,299</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OPPO Reno6 Pro 5G (Stellar Black, 256 GB)</td>\n",
              "      <td>39,990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>POCO M3 (Power Black, 64 GB)</td>\n",
              "      <td>11,499</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                Product Name  Prices\n",
              "0              realme C20 (Cool Grey, 32 GB)   6,999\n",
              "1              realme C20 (Cool Blue, 32 GB)   6,999\n",
              "2             REDMI 9i (Nature Green, 64 GB)   8,299\n",
              "3  OPPO Reno6 Pro 5G (Stellar Black, 256 GB)  39,990\n",
              "4               POCO M3 (Power Black, 64 GB)  11,499"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BMLBKb2d9ErD"
      },
      "source": [
        "df.to_csv('products.csv', index=False, encoding='utf-8')"
      ],
      "execution_count": 7,
      "outputs": []
    }
  ]
}