<jupyter_start><jupyter_text>Statistics pairplot<jupyter_code>import numpy as np
import pandas as pd
import seaborn as sns
dataframe = pd.read_csv("./ProcessedData/statistics_10_hamming.csv" , sep = ',')
dataframe['Tipo'][dataframe['Tipo'].values == 'Sano'] = 'Healthy'
dataframe.rename(columns={'Tipo':'Type'},
               inplace=True)
g = sns.pairplot(dataframe, hue = "Type", markers=["o", "s", "D"], corner = True)
g._legend.remove()
handles = g._legend_data.values()
labels = g._legend_data.keys()
g.fig.subplots_adjust(top=0.95, bottom=0.05)
g.fig.legend(handles=handles, labels=labels, loc='lower center', ncol=3, prop={'size':15})
g.savefig('statistics_pairplot', format='svg', dpi=1200)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.18106995884773663, 'fraction_numerical': 0.0205761316872428, 'mean_word_length': 6.849462365591398, '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': '393613', 'license': 'MIT', 'n_tokens_mistral': 284, 'n_tokens_neox': 270, 'n_words': 46, 'path': 'FailurePrediction/VariableRotationalSpeed/pairplot_statistics.ipynb', 'repo_name': 'judithspd/predictive-maintenance', 'size': '729'}