<jupyter_start><jupyter_text>Carga de datos a través de la función read_csv<jupyter_code>%config IPCompleter.greedy=True
import pandas as pd
mainpath="/Users/Jua"
data = pd.read_csv("../datasets/titanic/titanic3.csv",sep=",")
data.head()<jupyter_output><empty_output><jupyter_text>read.csv(filepath="/Users/JuanGabriel/Developer/AnaconadaProjects/python-ml-course/datasets/titanic/titanic3.csv",sep= ",", dtype=None{"a":np.float64,"edad":np.int32, header=0,names="ingresos","edad"}, skiprows=12,index_col=None,skip_blank_line=False, na_filter=False)<jupyter_code><jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.16526138279932545, 'fraction_numerical': 0.01517706576728499, 'mean_word_length': 11.122448979591837, 'pattern_counts': {'":': 2, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '362947', 'license': 'MIT', 'n_tokens_mistral': 244, 'n_tokens_neox': 237, 'n_words': 22, 'path': 'notebooks/Untitled1.ipynb', 'repo_name': 'JaquelineTorres/python-ml-course', 'size': '593'}