<jupyter_start><jupyter_text>Utilizamos esse notebook para criar um arquivo CSV que contém o nome do vereador e o "sequential_id"O nome do vereador está igual ao site da câmara de vereadores.Criamos esse arquivo pra poder juntar as informações do perfil do vereador e com as informações disponibilizadas no TSE que são identificadas pelo número sequencial<jupyter_code>import pandas as pd
seqs = [
    [250000011625, "<NAME>"],
    [250000019125, "<NAME>"],
    [250000004979, "<NAME>"],
    [250000022073, "<NAME>"],
    [250000022074, "Alfredinho"],
    [250000004981, "<NAME>"],
    [250000008293, "<NAME>"],
    [250000022075, "<NAME>"],
    [250000022076, "<NAME>"],
    [250000008347, "<NAME>"],
    [250000004982, "<NAME>"],
    [250000010990, "<NAME>"],
    [250000010991, "<NAME>"],
    [250000022114, "<NAME>"],
    [250000010847, "<NAME>"],
    [250000011011, "<NAME>"],
    [250000011013, "<NAME>"],
    [250000005003, "<NAME>"],
    [250000011016, "<NAME>"],
    [250000020825, "<NAME>"],
    [250000022081, "<NAME>"],
    [250000004992, "<NAME>"],
    [250000010992, "<NAME>"],
    [250000004984, "<NAME>"],
    [250000011018, "<NAME>"],
    [250000020779, "<NAME>"],
    [250000008375, "<NAME>"],
    [250000005006, "<NAME>"],
    [250000022120, "<NAME>"],
    [250000022084, "<NAME>"],
    [250000005798, "<NAME>"],
    [250000004997, "<NAME>"],
    [250000020837, "<NAME>"],
    [250000022086, "<NAME>"],
    [250000004989, "<NAME>"],
    [250000058381, "<NAME>"],
    [250000011022, "<NAME>"],
    [250000011624, "Natalini"],
    [250000022124, "<NAME>"],
    [250000010999, "Ota"],
    [250000004986, "<NAME>"],
    [250000019126, "<NAME>"],
    [250000004987, "Prof. <NAME>"],
    [250000004995, "<NAME>"],
    [250000011622, "<NAME>"],
    [250000022098, "Reis"],
    [250000020813, "<NAME>"],
    [250000022137, "<NAME>"],
    [250000008366, "<NAME>"],
    [250000010857, "<NAME>"],
    [250000020852, "<NAME>"],
    [250000020854, "<NAME>"],
    [250000014885, "<NAME>"],
    [250000011027, "<NAME>"],
    [250000022100, "<NAME>"],
    [250000010842, "Soninha"],
    [250000008365, "<NAME>"],
    [250000022111, "<NAME>"],
    [250000014868, "<NAME>"],
    [250000010863, "<NAME>"],
]
len(seqs)
df = pd.DataFrame(seqs, columns=['sequential_id', 'name'])
df.to_csv("../data/sequential_id.csv", index=False)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.2179813401187447, 'fraction_numerical': 0.3053435114503817, 'mean_word_length': 4.39816933638444, 'pattern_counts': {'":': 0, '<': 60, '<?xml version=': 0, '>': 60, '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': '135248', 'license': 'MIT', 'n_tokens_mistral': 1402, 'n_tokens_neox': 914, 'n_words': 184, 'path': 'atlas/notebooks/2017-04-01 gerenate sequential_id csv.ipynb', 'repo_name': 'novelo-io/atlas', 'size': '2776'}