<jupyter_start><jupyter_text>One Hot Encoding Variables<jupyter_code>import pandas as pd
import numpy as np
data=pd.read_csv('mercedesbenz.csv',usecols=['X1','X2','X3','X4','X5','X6'])
data.head()
for col in data.columns:
    print(col,':',len(data[col].unique()),'labels')<jupyter_output>X1 : 27 labels
X2 : 44 labels
X3 : 7 labels
X4 : 4 labels
X5 : 29 labels
X6 : 12 labels
{'fraction_non_alphanumeric': 0.1702127659574468, 'fraction_numerical': 0.05851063829787234, 'mean_word_length': 6.54, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, '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': '316385', 'license': 'CC0-1.0', 'n_tokens_mistral': 163, 'n_tokens_neox': 150, 'n_words': 35, 'path': 'One Hot Encoding Variables.ipynb', 'repo_name': 'Tanmoy741127/Python-ML', 'size': '376'}