Webscraping_Udemy.ipynb

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    "colab": {
      "name": "Webscraping_Udemy.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "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/jonkstro/Webscraping_Udemy/blob/main/Webscraping_Udemy.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# REALIZAR WEB SCRAPING DOS CURSOS DE PYTHON DA UDEMY"
      ],
      "metadata": {
        "id": "lYBTHk36hoVx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Importar as bibliotecas"
      ],
      "metadata": {
        "id": "74ueZOQfh1BP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fu-oclfidtKx"
      },
      "outputs": [],
      "source": [
        "cd /content/drive/MyDrive/Colab Notebooks/Webscraping_Udemy\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# importar os pacotes do webscraping\n",
        "\n",
        "from bs4 import BeautifulSoup\n",
        "import requests\n",
        "import time\n",
        "import random\n",
        "import pandas as pd\n",
        "from datetime import datetime"
      ],
      "metadata": {
        "id": "OHQdDSDPiWRk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Os códigos abaixo são para poder criar as variáveis de login, senha, local do trabalho e o cargo."
      ],
      "metadata": {
        "id": "2hFvLHLSOq1e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "s = requests.Session()\n",
        "cookies = s.get('https://www.udemy.com').cookies\n",
        "headers={\"Referer\": \"https://www.udemy.com/courses/search/?q=python&skip_price=false\"}\n",
        "\n",
        "print(s)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZiLP1VeGPKuf",
        "outputId": "3000f6e7-1da3-4972-9c16-01d771787684"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<requests.sessions.Session object at 0x7f47ca92ff10>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(columns = ['titulo' , 'preco', 'horas'])\n",
        "for page_counter in range(1, 5):\n",
        "    data = s.get('https://www.udemy.com/api-2.0/search-courses/?p={}&q=python'.format(page_counter), cookies=cookies, headers=headers).json()\n",
        "    for course in data['courses']:\n",
        "        params = {'course_ids': [str(course['id']),],\n",
        "              'fields/[pricing_result/]': ['price',]}\n",
        "        title = course['title']\n",
        "        hours = course['hrs_of_content_f']\n",
        "        price = s.get('https://www.udemy.com/api-2.0/pricing/', params=params, cookies=cookies).json()['courses'][str(course['id'])]['price']['amount']\n",
        "        #print({'title': title, 'price': price})\n",
        "        df=df.append({'titulo' : title, 'preco' : price, 'horas': hours} , ignore_index=True)\n",
        "df.head()"
      ],
      "metadata": {
        "id": "G7C5FyP0XW0t",
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          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "df0d79a2-24de-419d-cb00-971b4411e03a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              titulo  preco    horas\n",
              "0  100 Days of Code: The Complete Python Pro Boot...  14.99   64.047\n",
              "1  2022 Complete Python Bootcamp From Zero to Her...  14.99  22.2222\n",
              "2  Complete Python Developer in 2022: Zero to Mas...  14.99  30.7596\n",
              "3               Learn Python Programming Masterclass  14.99  70.6302\n",
              "4  Python for Data Science and Machine Learning B...  14.99  24.9106"
            ],
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              "      <th></th>\n",
              "      <th>titulo</th>\n",
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              "      <td>14.99</td>\n",
              "      <td>22.2222</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Complete Python Developer in 2022: Zero to Mas...</td>\n",
              "      <td>14.99</td>\n",
              "      <td>30.7596</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Learn Python Programming Masterclass</td>\n",
              "      <td>14.99</td>\n",
              "      <td>70.6302</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>14.99</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "#converter o df para csv\n",
        "dataHora = datetime.now().strftime('%Y%m%d%H%M')\n",
        "df.to_csv(f\"coleta{dataHora}.csv\",index=False)"
      ],
      "metadata": {
        "id": "WqMlrGm6gbXv"
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        ""
      ],
      "metadata": {
        "id": "pr1vLspMBxlI"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}