<jupyter_start><jupyter_text>Le Dé 1 est :<jupyter_code>de1<jupyter_output><empty_output><jupyter_text>Le DÉ 2 est :<jupyter_code>de2<jupyter_output><empty_output><jupyter_text>Le Dé 3 est :<jupyter_code>de3<jupyter_output><empty_output><jupyter_text>Le Dé 4 est :<jupyter_code>de4
limde1 = pd.DataFrame(index   = ["<NAME>"],
                      columns = ["0", "1", "2", "3", "4", "5"])
limde2 = pd.DataFrame(index   = ["DICE 2"],
                      columns = ["0", "1", "2", "3", "4", "5"])
limde3 = pd.DataFrame(index   = ["DICE 3"],
                      columns = ["0", "1", "2", "3", "4", "5"])
limde4 = pd.DataFrame(index   = ["DICE 4"],
                      columns = ["0", "1", "2", "3", "4", "5"])
des = pd.concat([limde1, de1.transpose(), limde2, de2.transpose(), limde3, de3.transpose(), limde4, de4.transpose()], axis=0)
des.to_csv("frequancedes.csv")<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.21444444444444444, 'fraction_numerical': 0.05333333333333334, 'mean_word_length': 3.717277486910995, 'pattern_counts': {'":': 0, '<': 18, '<?xml version=': 0, '>': 18, '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': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '482654', 'license': 'MIT', 'n_tokens_mistral': 383, 'n_tokens_neox': 374, 'n_words': 71, 'path': 'extractionlettre.ipynb', 'repo_name': 'Coincoin-Dataduck/Buzzer_Master_Dice_Frequency', 'size': '900'}