<jupyter_start><jupyter_text>1. Analisis inicial de parquet<jupyter_code>import pyarrow.parquet as pq
import os<jupyter_output><empty_output><jupyter_text>Cargamos el dataset<jupyter_code>from google.colab import drive
drive.mount('/content/drive')
# Miercoles no festivo
parquet_path = "/content/drive/MyDrive/CAFcoding/dataset/example_parquet/date=2020-11-18/"<jupyter_output><empty_output><jupyter_text>Analizamos Parquet<jupyter_code>parquet_file = pq.ParquetFile(os.path.join(parquet_path,"part-00008-tid-4502735041458916000-1def1e63-76d9-4841-bd06-f798a2422800-4252-2.c000.snappy.parquet"))

parquet_file.metadata<jupyter_output><empty_output><jupyter_text>**Como es version 1.0 tenemos que hacer un par de modificaciones en el fichero para ser capaces de tener resoluciones de milisegundos o microsegundos**<jupyter_code>dataset = pq.ParquetDataset(parquet_path)
print (dataset.schema)

table = dataset.read()
table.schema
type(table)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10607621009268794, 'fraction_numerical': 0.06797116374871266, 'mean_word_length': 8.818181818181818, 'pattern_counts': {'":': 0, '<': 17, '<?xml version=': 0, '>': 17, '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': '640278', 'license': 'Apache-2.0', 'n_tokens_mistral': 412, 'n_tokens_neox': 372, 'n_words': 54, 'path': 'documentation/6_1_analisis_parquet_caf_V2.ipynb', 'repo_name': 'maguelo/cafcoding', 'size': '971'}