<jupyter_start><jupyter_text>Automação de Sistemas e Processos com Python  Desafio- Entrar no sistema (no nosso caso, entrar no link)- Navegar até o local do relatório (entrar na pasta e exportar)- Fazer o download do relatório- Calcular os indicadores- Entrar no e-mail- Enviar por e-mail o resultado  Instalando o PyAutoGui<jupyter_code>!pip install pyautogui<jupyter_output>Collecting pyautogui
  Downloading PyAutoGUI-0.9.53.tar.gz (59 kB)
[K     |████████████████████████████████| 59 kB 461 kB/s eta 0:00:01
[?25hCollecting PyTweening>=1.0.1
  Downloading pytweening-1.0.4.tar.gz (14 kB)
Collecting mouseinfo
  Downloading MouseInfo-0.1.3.tar.gz (10 kB)
Collecting pygetwindow>=0.0.5
  Downloading PyGetWindow-0.0.9.tar.gz (9.7 kB)
Collecting pymsgbox
  Downloading PyMsgBox-1.0.9.tar.gz (18 kB)
  Installing build dependencies ... [?25ldone
[?25h  Getting requirements to build wheel ... [?25ldone
[?25h    Preparing wheel metadata ... [?25ldone
[?25hCollecting pyscreeze>=0.1.21
  Downloading PyScreeze-0.1.28.tar.gz (25 kB)
  Installing build dependencies ... [?25ldone
[?25h  Getting requirements to build wheel ... [?25ldone
[?25h    Preparing wheel metadata ... [?25ldone
[?25hCollecting python3-Xlib
  Downloading python3-xlib-0.15.tar.gz (132 kB)
[K     |████████████████████████████████| 132 kB 945 kB/s eta 0:00:01
[?25hCollecting pyperclip
  Dow[...]<jupyter_text>Instalando o PyPerClip<jupyter_code>!pip install pyperclip<jupyter_output>Requirement already satisfied: pyperclip in /home/medeiros/.local/lib/python3.8/site-packages (1.8.2)<jupyter_text>Importando as bibliotecas<jupyter_code>import pyautogui
import pyperclip
import time<jupyter_output><empty_output><jupyter_text>Seguindo os passos<jupyter_code># Entrar no sistema (no nosso caso, entrar no link)
pyautogui.PAUSE = 1
pyautogui.hotkey('ctrl', 't')
pyperclip.copy("https://www.linkedin.com/feed/?trk=nav_back_to_linkedin")
# pyautogui.write("https://www.linkedin.com/feed/?trk=nav_back_to_linkedin")
pyautogui.hotkey('ctrl', 'v')
pyautogui.press('enter')
time.sleep(5)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15458015267175573, 'fraction_numerical': 0.05057251908396947, 'mean_word_length': 5.37386018237082, 'pattern_counts': {'":': 0, '<': 15, '<?xml version=': 0, '>': 18, 'https://': 2, 'lorem ipsum': 0, 'www.': 2, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 8, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '286598', 'license': 'MIT', 'n_tokens_mistral': 936, 'n_tokens_neox': 835, 'n_words': 177, 'path': '01_Automacao.ipynb', 'repo_name': 'fabioeomedeiros/Data-Science-Base-II', 'size': '2096'}