Colab_Instagram_Conti_Motocross.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jopapo/instagram-post-crawler/blob/main/Colab_Instagram_Conti_Motocross.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Resumo\n",
        "\n",
        "Ferramenta de crawling de comentários do instagram (nesse caso dois post específicos) e busca de dados de quem comentou, o comentário e quando.\n",
        "\n",
        "Depois faz uma análise de cada conjunto de dados de cada post pra extrair os resultados esperados.\n",
        "\n",
        "## Limitações\n",
        "\n",
        "Não considera imagens nem replys.\n",
        "\n",
        "# 1. Preparação do ambiente\n",
        "\n",
        "Instalação de ferramentas básicas para poder fazer o crawling no instagram."
      ],
      "metadata": {
        "id": "m22q-8QJVYOH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "4-k52PrNfRXt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "92c18af9-6e17-4a2c-ca91-1496bc4676b0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Successfully installed cssselect-1.2.0 fuzzywuzzy-0.18.0 h11-0.14.0 outcome-1.3.0.post0 pyquery-2.0.0 selenium-4.16.0 trio-0.23.1 trio-websocket-0.11.1 wsproto-1.2.0\n",
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            "=> Installing the chromium snap\n",
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            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n",
            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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          ]
        }
      ],
      "source": [
        "!pip install selenium fuzzywuzzy pyquery\n",
        "!apt-get update\n",
        "!apt install chromium-chromedriver"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZubWdVFBaqeG"
      },
      "source": [
        "Configura um navegador pra rodar em background (Chrome) e simular um usuário"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "qybBGMP1_1QM"
      },
      "outputs": [],
      "source": [
        "from selenium import webdriver\n",
        "chrome_options = webdriver.ChromeOptions()\n",
        "chrome_options.add_argument('--headless')\n",
        "chrome_options.add_argument('--no-sandbox')\n",
        "chrome_options.add_argument('--disable-dev-shm-usage')\n",
        "chrome_options.add_argument('--kiosk')\n",
        "driver = webdriver.Chrome(chrome_options)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "djdEHQj6avNX"
      },
      "source": [
        "Dependências/bibliotecas necessárias à simulação"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "3xnYVkirtiVt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ce526c5-cc9a-42aa-a92c-75011cc9bced"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/fuzzywuzzy/fuzz.py:11: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning\n",
            "  warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')\n"
          ]
        }
      ],
      "source": [
        "import sys, time, csv, pandas as pd\n",
        "\n",
        "from getpass import getpass\n",
        "\n",
        "from selenium.webdriver.common.by import By\n",
        "from selenium.webdriver.support.wait import WebDriverWait\n",
        "from selenium.webdriver.support import expected_conditions as EC\n",
        "from selenium.common.exceptions import TimeoutException\n",
        "\n",
        "from IPython.display import Image, display, ProgressBar\n",
        "\n",
        "from fuzzywuzzy import fuzz\n",
        "from pyquery import PyQuery as pq\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Função de apoio pra mostrar imagens do selenium."
      ],
      "metadata": {
        "id": "j57UH2rFA4rs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "global image_counter\n",
        "image_counter = 0\n",
        "\n",
        "def show_browser(element = None):\n",
        "  global image_counter\n",
        "  image_counter = image_counter + 1\n",
        "  driver.save_screenshot(f'temp_{image_counter}.png')\n",
        "  display(Image(f'temp_{image_counter}.png'))\n",
        "\n",
        "  if element:\n",
        "    with open(f'temp_{image_counter}.html', 'w') as f:\n",
        "      f.write(driver.page_source if element == True else element.get_attribute('innerHtml'))\n",
        "\n",
        "base_path = '/content/drive/MyDrive/temp'"
      ],
      "metadata": {
        "id": "_9kcJOtx9hC2"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Autenticação no instagram\n",
        "\n",
        "Primeira batida. Precisa logar pra ter o privilégio de navegar nos comentários... mesmo público.\n",
        "(Aqui não tá 100% ainda, mas quebra um galo)"
      ],
      "metadata": {
        "id": "fvS7s4uNVfl3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "id": "Ytd8IZjNsHJ0",
        "outputId": "6d04930b-1d85-4977-8751-fbf48ee0097e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "username:hurrah_\n",
            "password:··········\n",
            "Logado\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "url=\"https://www.instagram.com/\"\n",
        "\n",
        "driver.get(url)\n",
        "time.sleep(2)\n",
        "\n",
        "try:\n",
        "  username = WebDriverWait(driver, 10).until(\n",
        "      EC.presence_of_element_located((By.NAME, \"username\"))\n",
        "  )\n",
        "\n",
        "  allow_cookies = driver.find_elements(By.XPATH, \"//*[text()='Allow all cookies']\")\n",
        "  if len(allow_cookies):\n",
        "    allow_cookies[0].click()\n",
        "\n",
        "  username.send_keys(input('username:'))\n",
        "\n",
        "  password = driver.find_element(By.NAME,\"password\")\n",
        "  password.send_keys(getpass('password:'))\n",
        "\n",
        "  password.submit()\n",
        "\n",
        "  try:\n",
        "    unusual_login = WebDriverWait(driver, 10).until(\n",
        "        EC.presence_of_element_located((By.XPATH, \"//*[text()='Send Security Code']\"))\n",
        "    )\n",
        "    show_browser()\n",
        "    unusual_login.click() #SMS\n",
        "  except TimeoutException:\n",
        "    pass\n",
        "\n",
        "  try:\n",
        "    security_code_input = WebDriverWait(driver, 10).until(\n",
        "        EC.any_of(\n",
        "          EC.presence_of_element_located((By.ID, \"security_code\")),\n",
        "          EC.presence_of_element_located((By.NAME, \"verificationCode\"))\n",
        "        )\n",
        "    )\n",
        "\n",
        "    security_code_input.send_keys(getpass('security_code'))\n",
        "    security_code_input.submit()\n",
        "  except TimeoutException:\n",
        "    pass\n",
        "\n",
        "  save_infos = driver.find_elements(By.XPATH, \"//*[text()='Save info']\")\n",
        "  if save_infos:\n",
        "    save_infos[0].click()\n",
        "\n",
        "  WebDriverWait(driver, 10).until(\n",
        "      EC.presence_of_element_located((By.CLASS_NAME,\"xw7yly9\"))\n",
        "  )\n",
        "\n",
        "  print('Logado')\n",
        "  show_browser()\n",
        "\n",
        "except:\n",
        "  show_browser(True)\n",
        "  raise"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Varredura dos posts\n",
        "\n",
        "A primeira parte é abrir a conexão com o drive pra não perder os arquivos gerados."
      ],
      "metadata": {
        "id": "qegzYLorWKjH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "OmcIUd28K1oG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf465ed0-b9e8-4bc7-8f5a-f6c1d15510c6"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Etapa mais longa. Aqui varre o que tem nas variáveis \"urls\". Esses são os posts a serem avaliados e grava num arquivo \"dump_X.html\"."
      ],
      "metadata": {
        "id": "pdrDVEQPrGRm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "we8a55F9gVLz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "548a9cdd-16fd-4dd6-b0fe-be6cacf99e2f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Navegando para: https://www.instagram.com/p/C0hO2UCO6KH/\n",
            "Pegando mais páginas de comentários: .............!\n",
            "Salvando o dump no diretório...\n",
            "Fim!\n"
          ]
        }
      ],
      "source": [
        "urls = [\n",
        "    \"https://www.instagram.com/p/C0hO2UCO6KH/\",\n",
        "    #\"https://www.instagram.com/p/C0hoEp5p3r_/\",\n",
        "]\n",
        "\n",
        "# Driver will access to the URL\n",
        "index = 0\n",
        "for url in urls:\n",
        "  index = index + 1\n",
        "  print('Navegando para:', url)\n",
        "  driver.get(url)\n",
        "  time.sleep(10)\n",
        "\n",
        "  try:\n",
        "    hasLoadMore = True\n",
        "    print(\"Pegando mais páginas de comentários: \", end='')\n",
        "    scroll_height = None\n",
        "    count = 0\n",
        "    while hasLoadMore:\n",
        "        count = count + 1\n",
        "        driver.execute_script(\"var el = document.querySelector('.x5yr21d.xw2csxc.x1odjw0f.x1n2onr6'); el.scrollTo(0, el.scrollHeight);\")\n",
        "        time.sleep(0.5)\n",
        "        print(\".\", end='')\n",
        "        if not (count % 100):\n",
        "          print(\";\")\n",
        "          driver.save_screenshot(f'scroll_{count}.png')\n",
        "          #break\n",
        "        progresbars = driver.find_elements(By.CSS_SELECTOR, '[role=progressbar]')\n",
        "        if not progresbars:\n",
        "          print(\"!\")\n",
        "          break\n",
        "\n",
        "    print(\"Salvando o dump no diretório...\")\n",
        "    with open(f\"{base_path}/dump_{index}.html\", 'w',  encoding='utf-8', newline='') as f:\n",
        "      f.write(driver.page_source)\n",
        "\n",
        "  except:\n",
        "    driver.save_screenshot('screenie.png')\n",
        "    raise\n",
        "\n",
        "print(\"Fim!\")\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Processa os dumps gerados em CSV.\n",
        "\n",
        "Ele grava o usuário, comentário e data/hora do comentário (ignora imagens e\n",
        "replys... por enquanto)\n"
      ],
      "metadata": {
        "id": "zA99qC7_tfkK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Varrendo, limpando e gravando os dados...\")\n",
        "\n",
        "# Create an empty container to put all the comments inside\n",
        "fields = [\"User\", \"Text\", \"Timestamp\", \"FileIndex\"]\n",
        "filename = f\"{base_path}/cleanfile.csv\"\n",
        "\n",
        "with open(filename, 'w',  encoding='utf-8', newline='') as csvfile:\n",
        "  csvwriter = csv.writer(csvfile)\n",
        "  csvwriter.writerow(fields)\n",
        "\n",
        "for index in range(1, len(urls)+1):\n",
        "    print(f\"Fazendo {index}... \", end='')\n",
        "\n",
        "    with open(f\"{base_path}/dump_{index}.html\", 'r',  encoding='utf-8', newline='') as f:\n",
        "      html = f.read() # local html\n",
        "      query = pq(html)\n",
        "\n",
        "    node_list = query('div.x5yr21d.xw2csxc.x1odjw0f.x1n2onr6').find('.x78zum5.xdt5ytf.x1iyjqo2').children('div')\n",
        "    print(f\"[{len(node_list)}] \", end='')\n",
        "\n",
        "    pbar_store = ProgressBar(len(node_list))\n",
        "    with open(filename, 'a',  encoding='utf-8', newline='') as csvfile:\n",
        "      csvwriter = csv.writer(csvfile)\n",
        "\n",
        "      for node in node_list:\n",
        "        pbar_store.progress = pbar_store.progress + 1\n",
        "        try:\n",
        "          user_name = pq('._ap3a._aaco._aacw._aad7._aade', node).text()\n",
        "          comment = pq('.x9f619.xjbqb8w.x78zum5.x168nmei.x13lgxp2.x5pf9jr.xo71vjh.x1uhb9sk.x1plvlek.xryxfnj.x1c4vz4f.x2lah0s.xdt5ytf.xqjyukv.x1cy8zhl.x1oa3qoh.x1nhvcw1', node).text()\n",
        "          timestamp = pq('time', node).attr('datetime')\n",
        "          if len(user_name) == 0:\n",
        "            continue\n",
        "        except:\n",
        "          print(node.html())\n",
        "          raise\n",
        "\n",
        "        csvwriter.writerow([user_name, comment, timestamp, index])\n"
      ],
      "metadata": {
        "id": "qZyXT6aFtcrZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "31d94558-ea4b-4590-a4e9-d3ffdcd00b28"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Varrendo, limpando e gravando os dados...\n",
            "Fazendo 1... [592] "
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[============================================================] 592/592"
            ],
            "text/html": [
              "<progress style='width:60ex' max='592' value='592'></progress>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w0Y3XaQAbZpv"
      },
      "source": [
        "Lê e mostra o que teve nos posts."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "EUV-3RZEkIGg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "7fd34538-07a6-442b-aede-d8f911ac9a4b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                      User                                  Text  \\\n",
              "0           faustomacieira  2T na veia - e no coração💙 Braaaaap!   \n",
              "1       adrimascarenhas211       Pra divertir 2T pra competir 4T   \n",
              "2               ado_jensen                             2t é vida   \n",
              "3               nichelalyo            Muito top essa moto 👍👍👍🏆🏆🏆   \n",
              "4           henzo_saviczk1                                  450🔥   \n",
              "..                     ...                                   ...   \n",
              "389            renilcajean                  Com certeza yz250 2t   \n",
              "390           kleniorobson                                 Yz250   \n",
              "391  alexpiressupersport56                                   450   \n",
              "392              thamy.sza                                YZ 250   \n",
              "393              _toni202_                                 Yz250   \n",
              "\n",
              "                    Timestamp  FileIndex  \n",
              "0    2023-12-06T17:17:48.000Z          1  \n",
              "1    2023-12-06T19:46:13.000Z          1  \n",
              "2    2023-12-07T09:17:58.000Z          1  \n",
              "3    2023-12-08T02:10:01.000Z          1  \n",
              "4    2023-12-06T17:51:54.000Z          1  \n",
              "..                        ...        ...  \n",
              "389  2023-12-09T12:56:13.000Z          1  \n",
              "390  2023-12-09T15:42:02.000Z          1  \n",
              "391  2023-12-09T22:21:26.000Z          1  \n",
              "392  2023-12-10T11:58:40.000Z          1  \n",
              "393  2023-12-10T13:40:48.000Z          1  \n",
              "\n",
              "[394 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c3c26a43-7c5e-476e-be65-a3084bde6f9b\" 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>User</th>\n",
              "      <th>Text</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>FileIndex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>faustomacieira</td>\n",
              "      <td>2T na veia - e no coração💙 Braaaaap!</td>\n",
              "      <td>2023-12-06T17:17:48.000Z</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>adrimascarenhas211</td>\n",
              "      <td>Pra divertir 2T pra competir 4T</td>\n",
              "      <td>2023-12-06T19:46:13.000Z</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ado_jensen</td>\n",
              "      <td>2t é vida</td>\n",
              "      <td>2023-12-07T09:17:58.000Z</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>nichelalyo</td>\n",
              "      <td>Muito top essa moto 👍👍👍🏆🏆🏆</td>\n",
              "      <td>2023-12-08T02:10:01.000Z</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>henzo_saviczk1</td>\n",
              "      <td>450🔥</td>\n",
              "      <td>2023-12-06T17:51:54.000Z</td>\n",
              "      <td>1</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>389</th>\n",
              "      <td>renilcajean</td>\n",
              "      <td>Com certeza yz250 2t</td>\n",
              "      <td>2023-12-09T12:56:13.000Z</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>390</th>\n",
              "      <td>kleniorobson</td>\n",
              "      <td>Yz250</td>\n",
              "      <td>2023-12-09T15:42:02.000Z</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>391</th>\n",
              "      <td>alexpiressupersport56</td>\n",
              "      <td>450</td>\n",
              "      <td>2023-12-09T22:21:26.000Z</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>392</th>\n",
              "      <td>thamy.sza</td>\n",
              "      <td>YZ 250</td>\n",
              "      <td>2023-12-10T11:58:40.000Z</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>_toni202_</td>\n",
              "      <td>Yz250</td>\n",
              "      <td>2023-12-10T13:40:48.000Z</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>394 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c3c26a43-7c5e-476e-be65-a3084bde6f9b')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-c3c26a43-7c5e-476e-be65-a3084bde6f9b 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-c3c26a43-7c5e-476e-be65-a3084bde6f9b');\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",
              "\n",
              "\n",
              "<div id=\"df-63b512cc-c247-466a-9c49-e93b46a59e16\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-63b512cc-c247-466a-9c49-e93b46a59e16')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-63b512cc-c247-466a-9c49-e93b46a59e16 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_0c0d87a7-b6ef-4340-b55f-7a1fa069cf35\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_0c0d87a7-b6ef-4340-b55f-7a1fa069cf35 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "df = pd.read_csv(f\"{base_path}/cleanfile.csv\")\n",
        "display(df)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Análise dos resultados"
      ],
      "metadata": {
        "id": "OWw3lYOBWZ4R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Qual você prefere nesta disputa? YZ250 ou YZ450F?"
      ],
      "metadata": {
        "id": "sxtk7Kjs3RDZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df[df.FileIndex == 1].copy() # 1 é o primeiro post da lista\n",
        "\n",
        "def detect_model(row):\n",
        "  if isinstance(row.Text, str):\n",
        "    if '450' in row.Text:\n",
        "      return '450'\n",
        "    elif '250' in row.Text:\n",
        "      return '250'\n",
        "  return None\n",
        "\n",
        "# Detecta seleção\n",
        "df1['Model'] = df1.apply(detect_model, axis = 1)\n",
        "\n",
        "# Remove duplicatas\n",
        "df1_uniq = df1[['User','Model']].drop_duplicates()\n",
        "\n",
        "print('Sumário:')\n",
        "df1_uniq['Model'].value_counts()"
      ],
      "metadata": {
        "id": "qtScw-jq36oT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e2609fd6-a6b1-4933-8fd9-094f250efd98"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sumário:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "250    87\n",
              "450    39\n",
              "Name: Model, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Verificando se a atribuição dos modelos estão corretos:"
      ],
      "metadata": {
        "id": "G41T-EdCUuYf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "display(df1)"
      ],
      "metadata": {
        "id": "qksaoL_9Uusq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "9155a9c8-c033-4a8a-819b-c103bd07295f"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                      User                                  Text  \\\n",
              "0           faustomacieira  2T na veia - e no coração💙 Braaaaap!   \n",
              "1       adrimascarenhas211       Pra divertir 2T pra competir 4T   \n",
              "2               ado_jensen                             2t é vida   \n",
              "3               nichelalyo            Muito top essa moto 👍👍👍🏆🏆🏆   \n",
              "4           henzo_saviczk1                                  450🔥   \n",
              "..                     ...                                   ...   \n",
              "389            renilcajean                  Com certeza yz250 2t   \n",
              "390           kleniorobson                                 Yz250   \n",
              "391  alexpiressupersport56                                   450   \n",
              "392              thamy.sza                                YZ 250   \n",
              "393              _toni202_                                 Yz250   \n",
              "\n",
              "                    Timestamp  FileIndex Model  \n",
              "0    2023-12-06T17:17:48.000Z          1  None  \n",
              "1    2023-12-06T19:46:13.000Z          1  None  \n",
              "2    2023-12-07T09:17:58.000Z          1  None  \n",
              "3    2023-12-08T02:10:01.000Z          1  None  \n",
              "4    2023-12-06T17:51:54.000Z          1   450  \n",
              "..                        ...        ...   ...  \n",
              "389  2023-12-09T12:56:13.000Z          1   250  \n",
              "390  2023-12-09T15:42:02.000Z          1   250  \n",
              "391  2023-12-09T22:21:26.000Z          1   450  \n",
              "392  2023-12-10T11:58:40.000Z          1   250  \n",
              "393  2023-12-10T13:40:48.000Z          1   250  \n",
              "\n",
              "[394 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ea827bef-d7f7-4439-8a30-c7d3ff2d9859\" 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>User</th>\n",
              "      <th>Text</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>FileIndex</th>\n",
              "      <th>Model</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>faustomacieira</td>\n",
              "      <td>2T na veia - e no coração💙 Braaaaap!</td>\n",
              "      <td>2023-12-06T17:17:48.000Z</td>\n",
              "      <td>1</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>adrimascarenhas211</td>\n",
              "      <td>Pra divertir 2T pra competir 4T</td>\n",
              "      <td>2023-12-06T19:46:13.000Z</td>\n",
              "      <td>1</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ado_jensen</td>\n",
              "      <td>2t é vida</td>\n",
              "      <td>2023-12-07T09:17:58.000Z</td>\n",
              "      <td>1</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>nichelalyo</td>\n",
              "      <td>Muito top essa moto 👍👍👍🏆🏆🏆</td>\n",
              "      <td>2023-12-08T02:10:01.000Z</td>\n",
              "      <td>1</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>henzo_saviczk1</td>\n",
              "      <td>450🔥</td>\n",
              "      <td>2023-12-06T17:51:54.000Z</td>\n",
              "      <td>1</td>\n",
              "      <td>450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>389</th>\n",
              "      <td>renilcajean</td>\n",
              "      <td>Com certeza yz250 2t</td>\n",
              "      <td>2023-12-09T12:56:13.000Z</td>\n",
              "      <td>1</td>\n",
              "      <td>250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>390</th>\n",
              "      <td>kleniorobson</td>\n",
              "      <td>Yz250</td>\n",
              "      <td>2023-12-09T15:42:02.000Z</td>\n",
              "      <td>1</td>\n",
              "      <td>250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>391</th>\n",
              "      <td>alexpiressupersport56</td>\n",
              "      <td>450</td>\n",
              "      <td>2023-12-09T22:21:26.000Z</td>\n",
              "      <td>1</td>\n",
              "      <td>450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>392</th>\n",
              "      <td>thamy.sza</td>\n",
              "      <td>YZ 250</td>\n",
              "      <td>2023-12-10T11:58:40.000Z</td>\n",
              "      <td>1</td>\n",
              "      <td>250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>_toni202_</td>\n",
              "      <td>Yz250</td>\n",
              "      <td>2023-12-10T13:40:48.000Z</td>\n",
              "      <td>1</td>\n",
              "      <td>250</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>394 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ea827bef-d7f7-4439-8a30-c7d3ff2d9859')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-ea827bef-d7f7-4439-8a30-c7d3ff2d9859 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-ea827bef-d7f7-4439-8a30-c7d3ff2d9859');\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",
              "\n",
              "\n",
              "<div id=\"df-f25cd870-ae91-4fa7-84ac-d6e87bcb8706\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f25cd870-ae91-4fa7-84ac-d6e87bcb8706')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f25cd870-ae91-4fa7-84ac-d6e87bcb8706 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_dc77c893-9edf-4e74-b9e4-967ef73c2ac1\">\n",
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              "      [theme=dark] .colab-df-generate {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df1')\"\n",
              "            title=\"Generate code using this dataframe.\"\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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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df1');\n",
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              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Qual piloto você escolhe para usar o colete dourado e ganhar um prêmio especial na Seletiva Final do Yamaha\n",
        "\n",
        "Importante: Atualmente ele remove votos do mesmo usuário para o mesmo piloto (drop_duplicates).\n"
      ],
      "metadata": {
        "id": "inKeKWPK68KQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Não sei os instas dos pilotos. Mas tem gente marcando ao invés de escrever o nome, daí deveria estar pro cara correto também.\n",
        "directs = {\n",
        "    '@pilotovinisouza': 'Vinícius Pereira Souza'\n",
        "    # falta bastante aqui\n",
        "}\n",
        "\n",
        "# Nomes possívels que será procurado no texto do comentário por aproximação.\n",
        "names = ['Alice Farias Matos','Alicia Sagae','Anna Vaz de Lima','Artur Aragão Everton',\n",
        "         'Benjamin Peralta','Bernardo Lacerda Brag','Caio Costa','Caio Eto Tiburcio',\n",
        "         'Carlos Andrade','Caua Anibal Batista da Silva','Cauã Santos','Celi Patricio',\n",
        "         'Clara Coutinho','Cristóbal Manuel Riveros Jeldez','Daniel Tomelin','Enzo Fialho',\n",
        "         'Enzo Matos Ximenes','Estevão Miranda e Silva','Fabrizio da Silva Souza Filho',\n",
        "         'Felipe Cartapatti Aoki','Gustavo Akio Honda','Gustavo Lima Moronari da Silva',\n",
        "         'Helena Oregana','Henri Oliveira Krug','Ignácio Gabriel Brant',\n",
        "         'Isabelly Vitoria Mombach','João Gabriel Ferreira Vilela',\n",
        "         'João Pedro Batista Barbosa','Kayque Mendes Pessini',\n",
        "         'Lorenzo Ritter Grassi','Miguel Garcia','Miguel Glir Pinheiro',\n",
        "         'Murilo Alves Motta','','Murilo Miwa Bastos','Pedro Lobo','Pedro Miguel Alves',\n",
        "         'Rheryncher José Heggendorn dos Santos','Salvador Cassini','Samuel Domanski',\n",
        "         'Samuel Taylor','Santiago Gossa','Santiago Vogel','Valentino Agustín Milone',\n",
        "         'Vinícius Pereira Souza','Yan Rodrigues Garcia']\n",
        "\n",
        "df2 = df[df.FileIndex == 2].copy() # 2 é o segundo post da lista\n",
        "\n",
        "def detect_name(row):\n",
        "  best_score = 0\n",
        "  best_name = None\n",
        "  if isinstance(row.Text, str) and len(row.Text) > 5:\n",
        "    for key, value in directs.items():\n",
        "      if key in row.Text:\n",
        "        return value\n",
        "    for name in names:\n",
        "      scored = 0\n",
        "      all_names = name.split(' ')\n",
        "      all_words = row.Text.split(' ')\n",
        "      for unique_name in all_names:\n",
        "        for unique_word in all_words:\n",
        "          ratio = fuzz.token_sort_ratio(unique_name, unique_word)\n",
        "          # Caso a palavra esteja \"90%\" próxima, pontua como melhor possibilidade.\n",
        "          if ratio > 90:\n",
        "            scored = scored + 1\n",
        "      if scored > best_score:\n",
        "        best_score = scored\n",
        "        best_name = name\n",
        "  return best_name\n",
        "\n",
        "# Detecta seleção\n",
        "df2['Name'] = df2.apply(detect_name, axis = 1)\n",
        "\n",
        "#display(df2)\n",
        "\n",
        "# Remove duplicatas\n",
        "df2_uniq = df2[['User','Name']].drop_duplicates()\n",
        "\n",
        "print('Sumário:')\n",
        "df2_uniq['Name'].value_counts()\n"
      ],
      "metadata": {
        "id": "qOzAlwFf7Ah8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60a7eb0e-6859-4e21-b450-ff770578ca5b"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sumário:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Series([], Name: Name, dtype: int64)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Verificação se a atribuição do nome está correta:"
      ],
      "metadata": {
        "id": "ignSQ6ULUoE7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "display(df2)"
      ],
      "metadata": {
        "id": "Xtmfw_9i_IHI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "923cfd79-ef1e-4954-f463-ce89d8eee6f5"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [User, Text, Timestamp, FileIndex, Name]\n",
              "Index: []"
            ],
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              "\n",
              "  <div id=\"df-b7ece415-61b9-4256-80e7-c8106ebd62aa\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "        vertical-align: top;\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th>Text</th>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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      "include_colab_link": true
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    "kernelspec": {
      "display_name": "Python 3",
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    "language_info": {
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