<jupyter_start><jupyter_text><jupyter_code>import pandas as pd
import warnings

warnings.filterwarnings('ignore')
#dados27 = pd.read_csv('/content/SEED - Semana {i}.csv' for i in range(27)

filenames = [f'/content/SEED - Semana {i}.csv' for i in range(37)]           

filenames = filenames[1:]
filenames
dados = [pd.read_csv(i) for i in filenames]
for i in range(36):
    dados[i] = dados[i].drop(columns=['email','cargo','teamId'])
dados[1].head()
for i in range(36):
  filenames = [f'dados{i}.csv' for i in range(36)]   
  dados[i].to_csv(filenames[i], index=False, encoding='UTF-8')
dados27.to_csv('dados27.csv', index=False, encoding='UTF-8')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1757754800590842, 'fraction_numerical': 0.029542097488921712, 'mean_word_length': 5.336448598130841, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '347626', 'license': 'MIT', 'n_tokens_mistral': 286, 'n_tokens_neox': 273, 'n_words': 55, 'path': 'tratamentoDosArquivos.ipynb', 'repo_name': 'marcelopaludetto/aulao-datascience', 'size': '677'}